**The Complete Android Kotlin Developer Course**

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 31.5 Hours | Lec: 205 | 4.45 GB

*Genre: eLearning | Language: English*

**Category: Tutorial** | **Comment: 0**

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 31.5 Hours | Lec: 205 | 4.45 GB

**Author: everest555**

**Category: Tutorial** | **Comment: 0**

Deep Learning: GANs And Variational Autoencoders (Updated 10/2018)

Video: .MP4, 1280x720 | Audio: AAC, 44kHz, 2ch | Duration: 7.5h

Genre: eLearning | Language: English | Size: 1,19 GB

Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow

What Will I Learn?

Learn the basic principles of generative models

Build a variational autoencoder in Theano and Tensorflow

Build a GAN (Generative Adversarial Network) in Theano and Tensorflow

Requirements

Know how to build a neural network in Theano and/or Tensorflow

Probability

Multivariate Calculus

Numpy, etc.

Description

Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently.

Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs.

GAN stands for generative adversarial network, where 2 neural networks compete with each other.

What is unsupervised learning?

Unsupervised learning means we're not trying to map input data to targets, we're just trying to learn the structure of that input data.

Once we've learned that structure, we can do some pretty cool things.

One example is generating poetry - we've done examples of this in the past.

But poetry is a very specific thing, how about writing in general?

If we can learn the structure of language, we can generate any kind of text. In fact, big companies are putting in lots of money to research how the news can be written by machines.

But what if we go back to poetry and take away the words?

Well then we get art, in general.

By learning the structure of art, we can create more art.

How about art as sound?

If we learn the structure of music, we can create new music.

Imagine the top 40 hits you hear on the radio are songs written by robots rather than humans.

The possibilities are endless!

You might be wondering, "how is this course different from the first unsupervised deep learning course?"

In this first course, we still tried to learn the structure of data, but the reasons were different.

We wanted to learn the structure of data in order to improve supervised training, which we demonstrated was possible.

In this new course, we want to learn the structure of data in order to produce more stuff that resembles the original data.

This by itself is really cool, but we'll also be incorporating ideas from Bayesian Machine Learning, Reinforcement Learning, and Game Theory. That makes it even cooler!

Thanks for reading and I'll see you in class. =)

NOTES:

/lazyprogrammer/machine_learning_examples

In the directory: unsupervised_class3

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

Calculus

Probability

Object-oriented programming

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations

Linear regression

Gradient descent

Know how to build a feedforward and convolutional neural network in Theano and TensorFlow

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

USEFUL COURSE ORDERING:

(The Numpy Stack in Python)

Linear Regression in Python

Logistic Regression in Python

(Supervised Machine Learning in Python)

(Bayesian Machine Learning in Python: A/B Testing)

Deep Learning in Python

Practical Deep Learning in Theano and TensorFlow

(Supervised Machine Learning in Python 2: Ensemble Methods)

Convolutional Neural Networks in Python

(Easy NLP)

(Cluster Analysis and Unsupervised Machine Learning)

Unsupervised Deep Learning

(Hidden Markov Models)

Recurrent Neural Networks in Python

Artificial Intelligence: Reinforcement Learning in Python

Natural Language Processing with Deep Learning in Python

Advanced AI: Deep Reinforcement Learning in Python

Deep Learning: GANs and Variational Autoencoders

Who is the target audience?

Anyone who wants to improve their deep learning knowledge

[i][/i]Screenshots

**I recommends Buy premimum account for High speed+parallel downloads!**

**Nitroflare**

http://nitroflare.com/view/698529F2086F600/Deep_Learning_GANs_and_Variational_Autoencoders.part1.rar

http://nitroflare.com/view/442EC62BCA6A0BE/Deep_Learning_GANs_and_Variational_Autoencoders.part2.rar

**Rapidgator**

https://rapidgator.net/file/b1f14805a0a6fbd7ee956d2b21a2fd92/Deep_Learning_GANs_and_Variational_Autoencoders.part1.rar.html

https://rapidgator.net/file/f8686cb0ff1656320845e8a6f8180bdb/Deep_Learning_GANs_and_Variational_Autoencoders.part2.rar.html

[/center]

Video: .MP4, 1280x720 | Audio: AAC, 44kHz, 2ch | Duration: 7.5h

Genre: eLearning | Language: English | Size: 1,19 GB

Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow

What Will I Learn?

Learn the basic principles of generative models

Build a variational autoencoder in Theano and Tensorflow

Build a GAN (Generative Adversarial Network) in Theano and Tensorflow

Requirements

Know how to build a neural network in Theano and/or Tensorflow

Probability

Multivariate Calculus

Numpy, etc.

Description

Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently.

Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs.

GAN stands for generative adversarial network, where 2 neural networks compete with each other.

What is unsupervised learning?

Unsupervised learning means we're not trying to map input data to targets, we're just trying to learn the structure of that input data.

Once we've learned that structure, we can do some pretty cool things.

One example is generating poetry - we've done examples of this in the past.

But poetry is a very specific thing, how about writing in general?

If we can learn the structure of language, we can generate any kind of text. In fact, big companies are putting in lots of money to research how the news can be written by machines.

But what if we go back to poetry and take away the words?

Well then we get art, in general.

By learning the structure of art, we can create more art.

How about art as sound?

If we learn the structure of music, we can create new music.

Imagine the top 40 hits you hear on the radio are songs written by robots rather than humans.

The possibilities are endless!

You might be wondering, "how is this course different from the first unsupervised deep learning course?"

In this first course, we still tried to learn the structure of data, but the reasons were different.

We wanted to learn the structure of data in order to improve supervised training, which we demonstrated was possible.

In this new course, we want to learn the structure of data in order to produce more stuff that resembles the original data.

This by itself is really cool, but we'll also be incorporating ideas from Bayesian Machine Learning, Reinforcement Learning, and Game Theory. That makes it even cooler!

Thanks for reading and I'll see you in class. =)

NOTES:

/lazyprogrammer/machine_learning_examples

In the directory: unsupervised_class3

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

Calculus

Probability

Object-oriented programming

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations

Linear regression

Gradient descent

Know how to build a feedforward and convolutional neural network in Theano and TensorFlow

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

USEFUL COURSE ORDERING:

(The Numpy Stack in Python)

Linear Regression in Python

Logistic Regression in Python

(Supervised Machine Learning in Python)

(Bayesian Machine Learning in Python: A/B Testing)

Deep Learning in Python

Practical Deep Learning in Theano and TensorFlow

(Supervised Machine Learning in Python 2: Ensemble Methods)

Convolutional Neural Networks in Python

(Easy NLP)

(Cluster Analysis and Unsupervised Machine Learning)

Unsupervised Deep Learning

(Hidden Markov Models)

Recurrent Neural Networks in Python

Artificial Intelligence: Reinforcement Learning in Python

Natural Language Processing with Deep Learning in Python

Advanced AI: Deep Reinforcement Learning in Python

Deep Learning: GANs and Variational Autoencoders

Who is the target audience?

Anyone who wants to improve their deep learning knowledge

[i][/i]Screenshots

http://nitroflare.com/view/698529F2086F600/Deep_Learning_GANs_and_Variational_Autoencoders.part1.rar

http://nitroflare.com/view/442EC62BCA6A0BE/Deep_Learning_GANs_and_Variational_Autoencoders.part2.rar

https://rapidgator.net/file/b1f14805a0a6fbd7ee956d2b21a2fd92/Deep_Learning_GANs_and_Variational_Autoencoders.part1.rar.html

https://rapidgator.net/file/f8686cb0ff1656320845e8a6f8180bdb/Deep_Learning_GANs_and_Variational_Autoencoders.part2.rar.html

[/center]

**Author: hunterlucky**

**Category: Tutorial** | **Comment: 0**

Data Science: Supervised Machine Learning in Python (Updated 10/2018)

Video: .MP4, 1280x720 | Audio: AAC, 48kHz, 2ch | Duration: 6h

Genre: eLearning | Language: English | Size: 0,99 GB

What Will I Learn?

Understand and implement K-Nearest Neighbors in Python

Understand the limitations of KNN

User KNN to solve several binary and multiclass classification problems

Understand and implement Naive Bayes and General Bayes Classifiers in Python

Understand the limitations of Bayes Classifiers

Understand and implement a Decision Tree in Python

Understand and implement the Perceptron in Python

Understand the limitations of the Perceptron

Understand hyperparameters and how to apply cross-validation

Understand the concepts of feature extraction and feature selection

Understand the pros and cons between classic machine learning methods and deep learning

Use Sci-Kit Learn

Implement a machine learning web service

Requirements

Python, Numpy, and Pandas experience

Probability and statistics (Gaussian distribution)

Strong ability to write algorithms

Description

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products.

Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It's extremely simple and intuitive, and it's a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we'll look at some ways in which KNN can fail.

It's important to know both the advantages and disadvantages of each algorithm we look at.

Next we'll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.

We'll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.

Next we'll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we'll study, and most courses you'll look at won't implement them. We will, since I believe implementation is good practice.

The last algorithm we'll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.

One we've studied these algorithms, we'll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.

We'll do a comparison with deep learning so you understand the pros and cons of each approach.

We'll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.

We'll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

NOTES:

In the directory: supervised_class

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)

Python coding: if/else, loops, lists, dicts, sets

Numpy, Scipy, Matplotlib

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

USEFUL COURSE ORDERING:

(The Numpy Stack in Python)

Linear Regression in Python

Logistic Regression in Python

(Supervised Machine Learning in Python)

(Bayesian Machine Learning in Python: A/B Testing)

Deep Learning in Python

Practical Deep Learning in Theano and TensorFlow

(Supervised Machine Learning in Python 2: Ensemble Methods)

Convolutional Neural Networks in Python

(Easy NLP)

(Cluster Analysis and Unsupervised Machine Learning)

Unsupervised Deep Learning

(Hidden Markov Models)

Recurrent Neural Networks in Python

Artificial Intelligence: Reinforcement Learning in Python

Natural Language Processing with Deep Learning in Python

Who is the target audience?

Students and professionals who want to apply machine learning techniques to their datasets

Students and professionals who want to apply machine learning techniques to real world problems

Anyone who wants to learn classic data science and machine learning algorithms

Anyone looking for an introduction to artificial intelligence (AI)

[i][/i]Screenshots

**I recommends Buy premimum account for High speed+parallel downloads!**

**Nitroflare**

http://nitroflare.com/view/63CE39A35249E1D/Data_Science_Supervised_Machine_Learning_in_Python.part1.rar

http://nitroflare.com/view/C2061E0C3E2B70A/Data_Science_Supervised_Machine_Learning_in_Python.part2.rar

**Rapidgator**

https://rapidgator.net/file/cfd9ec146f982c7d34f98a6f8c215473/Data_Science_Supervised_Machine_Learning_in_Python.part1.rar.html

https://rapidgator.net/file/8ea3e14aad16c5d7dc6ddcac3215aac5/Data_Science_Supervised_Machine_Learning_in_Python.part2.rar.html

[/center]

Video: .MP4, 1280x720 | Audio: AAC, 48kHz, 2ch | Duration: 6h

Genre: eLearning | Language: English | Size: 0,99 GB

What Will I Learn?

Understand and implement K-Nearest Neighbors in Python

Understand the limitations of KNN

User KNN to solve several binary and multiclass classification problems

Understand and implement Naive Bayes and General Bayes Classifiers in Python

Understand the limitations of Bayes Classifiers

Understand and implement a Decision Tree in Python

Understand and implement the Perceptron in Python

Understand the limitations of the Perceptron

Understand hyperparameters and how to apply cross-validation

Understand the concepts of feature extraction and feature selection

Understand the pros and cons between classic machine learning methods and deep learning

Use Sci-Kit Learn

Implement a machine learning web service

Requirements

Python, Numpy, and Pandas experience

Probability and statistics (Gaussian distribution)

Strong ability to write algorithms

Description

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products.

Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It's extremely simple and intuitive, and it's a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we'll look at some ways in which KNN can fail.

It's important to know both the advantages and disadvantages of each algorithm we look at.

Next we'll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.

We'll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.

Next we'll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we'll study, and most courses you'll look at won't implement them. We will, since I believe implementation is good practice.

The last algorithm we'll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.

One we've studied these algorithms, we'll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.

We'll do a comparison with deep learning so you understand the pros and cons of each approach.

We'll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.

We'll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

NOTES:

In the directory: supervised_class

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)

Python coding: if/else, loops, lists, dicts, sets

Numpy, Scipy, Matplotlib

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

USEFUL COURSE ORDERING:

(The Numpy Stack in Python)

Linear Regression in Python

Logistic Regression in Python

(Supervised Machine Learning in Python)

(Bayesian Machine Learning in Python: A/B Testing)

Deep Learning in Python

Practical Deep Learning in Theano and TensorFlow

(Supervised Machine Learning in Python 2: Ensemble Methods)

Convolutional Neural Networks in Python

(Easy NLP)

(Cluster Analysis and Unsupervised Machine Learning)

Unsupervised Deep Learning

(Hidden Markov Models)

Recurrent Neural Networks in Python

Artificial Intelligence: Reinforcement Learning in Python

Natural Language Processing with Deep Learning in Python

Who is the target audience?

Students and professionals who want to apply machine learning techniques to their datasets

Students and professionals who want to apply machine learning techniques to real world problems

Anyone who wants to learn classic data science and machine learning algorithms

Anyone looking for an introduction to artificial intelligence (AI)

[i][/i]Screenshots

http://nitroflare.com/view/63CE39A35249E1D/Data_Science_Supervised_Machine_Learning_in_Python.part1.rar

http://nitroflare.com/view/C2061E0C3E2B70A/Data_Science_Supervised_Machine_Learning_in_Python.part2.rar

https://rapidgator.net/file/cfd9ec146f982c7d34f98a6f8c215473/Data_Science_Supervised_Machine_Learning_in_Python.part1.rar.html

https://rapidgator.net/file/8ea3e14aad16c5d7dc6ddcac3215aac5/Data_Science_Supervised_Machine_Learning_in_Python.part2.rar.html

[/center]

**Author: hunterlucky**

**Category: Tutorial** | **Comment: 0**

Data Science: Deep Learning in Python (Updated)

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Genre: eLearning | Language: English + .VTT | Duration: 9.5 hour | Size: 1.43 GB

The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow

What you'll learn

Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)

Learn how a neural network is built from basic building blocks (the neuron)

Code a neural network from scratch in Python and numpy

Code a neural network using Google's TensorFlow

Describe different types of neural networks and the different types of problems they are used for

Derive the backpropagation rule from first principles

Create a neural network with an output that has K > 2 classes using softmax

Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"

Install TensorFlow

Requirements

How to take partial derivatives and log-likelihoods (ex. finding the maximum likelihood estimations for a die)

Install Numpy and Python (approx. latest version of Numpy as of Jan 2016)

Don't worry about installing TensorFlow, we will do that in the lectures.

Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course

Description

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google's new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for.

NOTE:

If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow.

I have other courses that cover more advanced topics, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

linear algebra

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course

Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

People who already know how to take partial derivatives and log-likelihoods. Since we cover this in more detail in my logistic regression class, it is not covered quite as thoroughly here.

People who already know how to code in Python and Numpy. You will need some familiarity because we go through it quite fast. Don't worry, it's not that hard.

[i][/i]Screenshots

**I recommends Buy premimum account for High speed+parallel downloads!**

**Nitroflare**

http://nitroflare.com/view/5D46D8A71FD0754/Data_Science_Deep_Learning_in_Python.part1.rar

http://nitroflare.com/view/B332160B4B76249/Data_Science_Deep_Learning_in_Python.part2.rar

**Rapidgator**

https://rapidgator.net/file/57c57664526fbbe816b19cd52ae83910/Data_Science_Deep_Learning_in_Python.part1.rar.html

https://rapidgator.net/file/c7dcb5ac599a2087a245ebf8da0a5aa5/Data_Science_Deep_Learning_in_Python.part2.rar.html

[/center]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Genre: eLearning | Language: English + .VTT | Duration: 9.5 hour | Size: 1.43 GB

The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow

What you'll learn

Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)

Learn how a neural network is built from basic building blocks (the neuron)

Code a neural network from scratch in Python and numpy

Code a neural network using Google's TensorFlow

Describe different types of neural networks and the different types of problems they are used for

Derive the backpropagation rule from first principles

Create a neural network with an output that has K > 2 classes using softmax

Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"

Install TensorFlow

Requirements

How to take partial derivatives and log-likelihoods (ex. finding the maximum likelihood estimations for a die)

Install Numpy and Python (approx. latest version of Numpy as of Jan 2016)

Don't worry about installing TensorFlow, we will do that in the lectures.

Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course

Description

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google's new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for.

NOTE:

If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow.

I have other courses that cover more advanced topics, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

linear algebra

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course

Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

People who already know how to take partial derivatives and log-likelihoods. Since we cover this in more detail in my logistic regression class, it is not covered quite as thoroughly here.

People who already know how to code in Python and Numpy. You will need some familiarity because we go through it quite fast. Don't worry, it's not that hard.

[i][/i]Screenshots

http://nitroflare.com/view/5D46D8A71FD0754/Data_Science_Deep_Learning_in_Python.part1.rar

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[/center]

**Author: hunterlucky**

**Category: Tutorial** | **Comment: 0**

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3.5 Hours | Lec: 35 | 613 MB

**Author: everest555**

**Category: Tutorial** | **Comment: 0**

Learn DevOps: The Complete Kubernetes Course (Updated)

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Genre: eLearning | Language: English + .VTT | Duration: 11.5 hour | Size: 3.68 GB

Kubernetes will run and manage your containerized applications. Learn how to build, deploy, use, and maintain Kubernetes

What you'll learn

Install and configure Kubernetes (on your laptop/desktop or production grade cluster on AWS)

Use Docker Client (with kubernetes), kubeadm, kops, or minikube to setup your cluster

Be able to run stateless and stateful applications on Kubernetes

Use Healthchecks, Secrets, ConfigMaps, placement strategies using Node/Pod affinity / anti-affinity

Use StatefulSets to deploy a Cassandra cluster on Kubernetes

Add users, set quotas/limits, do node maintenance, setup monitoring

Use Volumes to provide persistence to your containers

Be able to scale your apps using metrics

Package applications with Helm and write your own Helm charts for your applications

Automatically build and deploy your own Helm Charts using Jenkins

Install and use kubeless to run functions (Serverless) on Kubernetes

Install and use Istio to deploy a service mesh on Kubernetes

Requirements

The first lectures in the course will explain how to install the software. You can choose between a local setup (docker client with kubernetes or minikube), or a full production grade cluster on AWS

If you want to install Kubernetes on-prem, there are lectures available in this course covering kubeadm, which can install kubernetes on a wide variety of environments

Knowledge about Linux / Docker / AWS is a plus, but not mandatory to be able to do the course

Description

This course will help you to gain understanding how to deploy, use, and maintain your applications on Kubernetes. If you are into DevOps, this is a technology you need to master. Kubernetes has gained a lot of popularity lately and it is a well sought skill by companies.

This course is updated frequently to include the features of latest releases!

When Google started running containers a decade ago, nobody could reach this kind of infrastructure agility and efficiency. Using this knowledge, Google released Kubernetes as an free and open source project. Nowadays Kubernetes is used by small companies and big enterprises who want to gain the efficiency and velocity Google has.

You can containerize applications using Docker. You can then run those containers on your servers, but there's no way you can manage those efficiently without extra management software. Kubernetes is an orchestrator for your containers that will create, schedule and manage your containers on a cluster of servers. Kubernetes can run on-premise or in the cloud, on a single machine or on thousands of machines.

I will show you how to build apps in containers using docker and how to deploy those on a Kubernetes cluster. I will explain you how to setup your cluster on your desktop, or on the cloud using AWS. I use a real world example app (Wordpress with MySQL - blogging software) to show you the real power of Kubernetes: scheduling stateless and stateful applications.

The introduction lectures that show you the Kubernetes desktop installation are free to preview, so you can already have a go at it before buying the course!

This course also has Closed Captions (English subtitles)

Who this course is for:

There is no prior knowledge needed, but a dev/ops/cloud/linux/networks background will definitely help

The course optionally uses Kubernetes on AWS. If you want to learn more about AWS itself, you will need to read some AWS documentation or take another AWS course. The course only explains how to use Kubernetes on AWS, it doesn't explain how to use AWS itself. Still, all steps that you need to follow are explained in this course.

[i][/i]Screenshots

**I recommends Buy premimum account for High speed+parallel downloads!**

**Nitroflare**

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[/center]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Genre: eLearning | Language: English + .VTT | Duration: 11.5 hour | Size: 3.68 GB

Kubernetes will run and manage your containerized applications. Learn how to build, deploy, use, and maintain Kubernetes

What you'll learn

Install and configure Kubernetes (on your laptop/desktop or production grade cluster on AWS)

Use Docker Client (with kubernetes), kubeadm, kops, or minikube to setup your cluster

Be able to run stateless and stateful applications on Kubernetes

Use Healthchecks, Secrets, ConfigMaps, placement strategies using Node/Pod affinity / anti-affinity

Use StatefulSets to deploy a Cassandra cluster on Kubernetes

Add users, set quotas/limits, do node maintenance, setup monitoring

Use Volumes to provide persistence to your containers

Be able to scale your apps using metrics

Package applications with Helm and write your own Helm charts for your applications

Automatically build and deploy your own Helm Charts using Jenkins

Install and use kubeless to run functions (Serverless) on Kubernetes

Install and use Istio to deploy a service mesh on Kubernetes

Requirements

The first lectures in the course will explain how to install the software. You can choose between a local setup (docker client with kubernetes or minikube), or a full production grade cluster on AWS

If you want to install Kubernetes on-prem, there are lectures available in this course covering kubeadm, which can install kubernetes on a wide variety of environments

Knowledge about Linux / Docker / AWS is a plus, but not mandatory to be able to do the course

Description

This course will help you to gain understanding how to deploy, use, and maintain your applications on Kubernetes. If you are into DevOps, this is a technology you need to master. Kubernetes has gained a lot of popularity lately and it is a well sought skill by companies.

This course is updated frequently to include the features of latest releases!

When Google started running containers a decade ago, nobody could reach this kind of infrastructure agility and efficiency. Using this knowledge, Google released Kubernetes as an free and open source project. Nowadays Kubernetes is used by small companies and big enterprises who want to gain the efficiency and velocity Google has.

You can containerize applications using Docker. You can then run those containers on your servers, but there's no way you can manage those efficiently without extra management software. Kubernetes is an orchestrator for your containers that will create, schedule and manage your containers on a cluster of servers. Kubernetes can run on-premise or in the cloud, on a single machine or on thousands of machines.

I will show you how to build apps in containers using docker and how to deploy those on a Kubernetes cluster. I will explain you how to setup your cluster on your desktop, or on the cloud using AWS. I use a real world example app (Wordpress with MySQL - blogging software) to show you the real power of Kubernetes: scheduling stateless and stateful applications.

The introduction lectures that show you the Kubernetes desktop installation are free to preview, so you can already have a go at it before buying the course!

This course also has Closed Captions (English subtitles)

Who this course is for:

There is no prior knowledge needed, but a dev/ops/cloud/linux/networks background will definitely help

The course optionally uses Kubernetes on AWS. If you want to learn more about AWS itself, you will need to read some AWS documentation or take another AWS course. The course only explains how to use Kubernetes on AWS, it doesn't explain how to use AWS itself. Still, all steps that you need to follow are explained in this course.

[i][/i]Screenshots

http://nitroflare.com/view/AC86FC62994D70E/Learn_DevOps_The_Complete_Kubernetes_Course.part1.rar

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[/center]

**Author: hunterlucky**

**Category: Tutorial** | **Comment: 0**

Leading the Organization Monthly (Updated 2/25/2019)

MP4 | Video: h264, 1280x720 | Audio: AAC, 48 KHz, 2 Ch | Duration: 20m 51s

Genre: eLearning | Language: English + Sub | Size: 165 MB

Moving to the C-Suite requires a shift in mindset. The paradox is that it means leading less and delegating more, to create an environment where people are inspired to do their best. For those transitioning from leading others to leading the organization, this course offers tips to adapt to the realities of an executive role and move your leadership abilities forward. Learn how to inspire confidence, delegate work, identify talent, lead multiple generations, and build a company vision, strategy, and culture. Each month, consultant and former CEO Jan Rutherford provides new tips that will help you succeed in a new C-Suite role.

[i][/i]Screenshots

**I recommends Buy premimum account for High speed+parallel downloads!**

**Nitroflare**

**Rapidgator**

[/center]

MP4 | Video: h264, 1280x720 | Audio: AAC, 48 KHz, 2 Ch | Duration: 20m 51s

Genre: eLearning | Language: English + Sub | Size: 165 MB

Moving to the C-Suite requires a shift in mindset. The paradox is that it means leading less and delegating more, to create an environment where people are inspired to do their best. For those transitioning from leading others to leading the organization, this course offers tips to adapt to the realities of an executive role and move your leadership abilities forward. Learn how to inspire confidence, delegate work, identify talent, lead multiple generations, and build a company vision, strategy, and culture. Each month, consultant and former CEO Jan Rutherford provides new tips that will help you succeed in a new C-Suite role.

[i][/i]Screenshots

[/center]

**Author: hunterlucky**

**Category: Tutorial** | **Comment: 0**

Java In-Depth: Become a Complete Java Engineer! (Updated 2/2019)

Video: .MP4, 1280x720 | Audio: AAC, 44kHz, 2ch | Duration: 62h 40m

Genre: eLearning | Language: English | Size: 12,5 GB

Comprehensive Java programming course integrated with design principles, best practices & instructor-led Java EE project

What you'll learn

Get an in-depth understanding of core & advanced Java

Master design principles, best practices and coding conventions for writing well-designed, professional Java code

Implement instructor-led, professional-grade Java EE-based Web application using TDD principles and MySQL as database

Set a firm foundation in Java for the rest of your career

Gain comprehensive understanding of JVM Internals ~ the incredible platform on which Java programs run

Set yourself up to become an Oracle Certified Associate, Java SE 8 Programmer (1Z0-808)

Master Object-Oriented Programming concepts by using a real-world application as a case study

Get a solid understanding of functional-style programming using Java 8 constructs like lambdas & streams

[i][/i]Screenshots

**I recommends Buy premimum account for High speed+parallel downloads!**

**Nitroflare**

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[/center]

Video: .MP4, 1280x720 | Audio: AAC, 44kHz, 2ch | Duration: 62h 40m

Genre: eLearning | Language: English | Size: 12,5 GB

Comprehensive Java programming course integrated with design principles, best practices & instructor-led Java EE project

What you'll learn

Get an in-depth understanding of core & advanced Java

Master design principles, best practices and coding conventions for writing well-designed, professional Java code

Implement instructor-led, professional-grade Java EE-based Web application using TDD principles and MySQL as database

Set a firm foundation in Java for the rest of your career

Gain comprehensive understanding of JVM Internals ~ the incredible platform on which Java programs run

Set yourself up to become an Oracle Certified Associate, Java SE 8 Programmer (1Z0-808)

Master Object-Oriented Programming concepts by using a real-world application as a case study

Get a solid understanding of functional-style programming using Java 8 constructs like lambdas & streams

[i][/i]Screenshots

http://nitroflare.com/view/11C62E1FF3E8D70/Java_In-Depth_Become_a_Complete_Java_Engineer%21.part01.rar

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[/center]

**Author: hunterlucky**

**Category: Tutorial** | **Comment: 0**

Natural Language Processing with Deep Learning in Python (Updated)

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Genre: eLearning | Language: English + .VTT | Duration: 13 hour | Size: 3.12 GB

Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets

What you'll learn

Understand and implement word2vec

Understand the CBOW method in word2vec

Understand the skip-gram method in word2vec

Understand the negative sampling optimization in word2vec

Understand and implement GloVe using gradient descent and alternating least squares

Use recurrent neural networks for parts-of-speech tagging

Use recurrent neural networks for named entity recognition

Understand and implement recursive neural networks for sentiment analysis

Understand and implement recursive neural tensor networks for sentiment analysis

Requirements

Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now)

Understand backpropagation and gradient descent, be able to derive and code the equations on your own

Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function

Code a feedforward neural network in Theano (or Tensorflow)

Helpful to have experience with tree algorithms

Description

In this course we are going to look at advanced NLP.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.

These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.

In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.

First up is word2vec.

In this course, I'm going to show you exactly how word2vec works, from theory to implementation, and you'll see that it's merely the application of skills you already know.

Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:

king - man = queen - woman

France - Paris = England - London

December - Novemeber = July - June

We are also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.

Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it's way easier to train.

We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You'll see that just about any problem can be solved using neural networks, but you'll also learn the dangers of having too much complexity.

Lastly, you'll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

See you in class!

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

linear algebra

probability (conditional and joint distributions)

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

neural networks and backpropagation, be able to derive and code gradient descent algorithms on your own

Can write a feedforward neural network in Theano and TensorFlow

Can write a recurrent neural network / LSTM / GRU in Theano and TensorFlow from basic primitives, especially the scan function

Helpful to have experience with tree algorithms

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

Students and professionals who want to create word vector representations for various NLP tasks

Students and professionals who are interested in state-of-the-art neural network architectures like recursive neural networks

SHOULD NOT: Anyone who is not comfortable with the prerequisites.

[i][/i]Screenshots

**I recommends Buy premimum account for High speed+parallel downloads!**

**Nitroflare**

http://nitroflare.com/view/FEF707381ECE2B1/Natural_Language_Processing_with_Deep_Learning_in_Python.part1.rar

http://nitroflare.com/view/08331DBCAADBDC1/Natural_Language_Processing_with_Deep_Learning_in_Python.part2.rar

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[/center]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Genre: eLearning | Language: English + .VTT | Duration: 13 hour | Size: 3.12 GB

Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets

What you'll learn

Understand and implement word2vec

Understand the CBOW method in word2vec

Understand the skip-gram method in word2vec

Understand the negative sampling optimization in word2vec

Understand and implement GloVe using gradient descent and alternating least squares

Use recurrent neural networks for parts-of-speech tagging

Use recurrent neural networks for named entity recognition

Understand and implement recursive neural networks for sentiment analysis

Understand and implement recursive neural tensor networks for sentiment analysis

Requirements

Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now)

Understand backpropagation and gradient descent, be able to derive and code the equations on your own

Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function

Code a feedforward neural network in Theano (or Tensorflow)

Helpful to have experience with tree algorithms

Description

In this course we are going to look at advanced NLP.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.

These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.

In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.

First up is word2vec.

In this course, I'm going to show you exactly how word2vec works, from theory to implementation, and you'll see that it's merely the application of skills you already know.

Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:

king - man = queen - woman

France - Paris = England - London

December - Novemeber = July - June

We are also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.

Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it's way easier to train.

We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You'll see that just about any problem can be solved using neural networks, but you'll also learn the dangers of having too much complexity.

Lastly, you'll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

See you in class!

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

linear algebra

probability (conditional and joint distributions)

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

neural networks and backpropagation, be able to derive and code gradient descent algorithms on your own

Can write a feedforward neural network in Theano and TensorFlow

Can write a recurrent neural network / LSTM / GRU in Theano and TensorFlow from basic primitives, especially the scan function

Helpful to have experience with tree algorithms

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

Students and professionals who want to create word vector representations for various NLP tasks

Students and professionals who are interested in state-of-the-art neural network architectures like recursive neural networks

SHOULD NOT: Anyone who is not comfortable with the prerequisites.

[i][/i]Screenshots

http://nitroflare.com/view/FEF707381ECE2B1/Natural_Language_Processing_with_Deep_Learning_in_Python.part1.rar

http://nitroflare.com/view/08331DBCAADBDC1/Natural_Language_Processing_with_Deep_Learning_in_Python.part2.rar

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https://rapidgator.net/file/994ab3c2351119e22f63ec88a2b5f0de/Natural_Language_Processing_with_Deep_Learning_in_Python.part2.rar.html

https://rapidgator.net/file/e0b6f22f08dfedefb37c5cc863b209fa/Natural_Language_Processing_with_Deep_Learning_in_Python.part3.rar.html

https://rapidgator.net/file/de73d556a173dc3c23be6d9dc60b04e1/Natural_Language_Processing_with_Deep_Learning_in_Python.part4.rar.html

[/center]

**Author: hunterlucky**

**Category: Tutorial** | **Comment: 0**

Bayesian Machine Learning in Python: A/B Testing (Updated)

MP4 | Video: h264, 1280x720 | Audio: AAC, 48 KHz, 2 Ch

Genre: eLearning | Language: English + .VTT | Duration: 5.5 hour | Size: 853 MB

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More

What you'll learn

Use adaptive algorithms to improve A/B testing performance

Understand the difference between Bayesian and frequentist statistics

Apply Bayesian methods to A/B testing

Requirements

Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)

Python coding with the Numpy stack

Description

This course is all about A/B testing.

A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more.

A/B testing is all about comparing things.

If you're a data scientist, and you want to tell the rest of the company, "logo A is better than logo B", well you can't just say that without proving it using numbers and statistics.

Traditional A/B testing has been around for a long time, and it's full of approximations and confusing definitions.

In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.

First, we'll see if we can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma.

You'll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning.

We'll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.

Finally, we'll improve on both of those by using a fully Bayesian approach.

Why is the Bayesian method interesting to us in machine learning?

It's an entirely different way of thinking about probability.

It's a paradigm shift.

You'll probably need to come back to this course several times before it fully sinks in.

It's also powerful, and many machine learning experts often make statements about how they "subscribe to the Bayesian school of thought".

In sum - it's going to give us a lot of powerful new tools that we can use in machine learning.

The things you'll learn in this course are not only applicable to A/B testing, but rather, we're using A/B testing as a concrete example of how Bayesian techniques can be applied.

You'll learn these fundamental tools of the Bayesian method - through the example of A/B testing - and then you'll be able to carry those Bayesian techniques to more advanced machine learning models in the future.

See you in class!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)

Python coding: if/else, loops, lists, dicts, sets

Numpy, Scipy, Matplotlib

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

Students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work

[i][/i]Screenshots

**I recommends Buy premimum account for High speed+parallel downloads!**

**Nitroflare**

http://nitroflare.com/view/3C3A147E87E05CF/Bayesian_Machine_Learning_in_Python_AB_Testing.part1.rar

http://nitroflare.com/view/BD1E130A55FF933/Bayesian_Machine_Learning_in_Python_AB_Testing.part2.rar

**Rapidgator**

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https://rapidgator.net/file/32f092149fbbfbddb5c48ed0d01481a8/Bayesian_Machine_Learning_in_Python_AB_Testing.part2.rar.html

[/center]

MP4 | Video: h264, 1280x720 | Audio: AAC, 48 KHz, 2 Ch

Genre: eLearning | Language: English + .VTT | Duration: 5.5 hour | Size: 853 MB

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More

What you'll learn

Use adaptive algorithms to improve A/B testing performance

Understand the difference between Bayesian and frequentist statistics

Apply Bayesian methods to A/B testing

Requirements

Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)

Python coding with the Numpy stack

Description

This course is all about A/B testing.

A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more.

A/B testing is all about comparing things.

If you're a data scientist, and you want to tell the rest of the company, "logo A is better than logo B", well you can't just say that without proving it using numbers and statistics.

Traditional A/B testing has been around for a long time, and it's full of approximations and confusing definitions.

In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.

First, we'll see if we can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma.

You'll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning.

We'll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.

Finally, we'll improve on both of those by using a fully Bayesian approach.

Why is the Bayesian method interesting to us in machine learning?

It's an entirely different way of thinking about probability.

It's a paradigm shift.

You'll probably need to come back to this course several times before it fully sinks in.

It's also powerful, and many machine learning experts often make statements about how they "subscribe to the Bayesian school of thought".

In sum - it's going to give us a lot of powerful new tools that we can use in machine learning.

The things you'll learn in this course are not only applicable to A/B testing, but rather, we're using A/B testing as a concrete example of how Bayesian techniques can be applied.

You'll learn these fundamental tools of the Bayesian method - through the example of A/B testing - and then you'll be able to carry those Bayesian techniques to more advanced machine learning models in the future.

See you in class!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)

Python coding: if/else, loops, lists, dicts, sets

Numpy, Scipy, Matplotlib

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

Students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work

[i][/i]Screenshots

http://nitroflare.com/view/3C3A147E87E05CF/Bayesian_Machine_Learning_in_Python_AB_Testing.part1.rar

http://nitroflare.com/view/BD1E130A55FF933/Bayesian_Machine_Learning_in_Python_AB_Testing.part2.rar

https://rapidgator.net/file/24fd4fa362153af395066da497946fa9/Bayesian_Machine_Learning_in_Python_AB_Testing.part1.rar.html

https://rapidgator.net/file/32f092149fbbfbddb5c48ed0d01481a8/Bayesian_Machine_Learning_in_Python_AB_Testing.part2.rar.html

[/center]

**Author: hunterlucky**

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