Udacity Natural Language Processing Nanodegree Nd892 V1 0 0

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Udacity   Natural Language Processing Nanodegree Nd892 V1 0 0
Udacity - Natural Language Processing Nanodegree nd892 v1.0.0
English | Size: 1.78 GB
Category: CBT


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Digital Signal Processing (dsp) From Ground Upô In Python

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Digital Signal Processing (dsp) From Ground Upô In Python
Digital Signal Processing (DSP) From Ground Upô in Python
.MP4, AVC, 1000 kbps, 1280x720 | English, AAC, 64 kbps, 2 Ch | 12h 46m | 5.82 GB
Created by Israel Gbati


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Natural Language Processing with Deep Learning in Python (Updated)

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Natural Language Processing with Deep Learning in Python (Updated)

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.
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NLP - Natural Language Processing with Python

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NLP - Natural Language Processing with Python

NLP - Natural Language Processing with Python

NLP - Natural Language Processing with Python
$200 | BESTSELLER | Last Updated 1/2019
Duration: 11.5 hours | Video: h264, 1280x720 | Audio: AAC, 44 KHz, 2 Ch | 4.5 GB
Genre: eLearning | Language: English + Sub | 79 Lectures

Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing

What you'll learn
Learn to work with Text Files with Python
Learn how to work with PDF files in Python
Utilize Regular Expressions for pattern searching in text
Use Spacy for ultra fast tokenization
Learn about Stemming and Lemmatization
Understand Vocabulary Matching with Spacy
Use Part of Speech Tagging to automatically process raw text files
Understand Named Entity Recognition
Visualize POS and NER with Spacy
Use SciKit-Learn for Text Classification
Use Latent Dirichlet Allocation for Topic Modelling
Learn about Non-negative Matrix Factorization
Use the Word2Vec algorithm
Use NLTK for Sentiment Analysis
Use Deep Learning to build out your own chat bot

Requirements
Understand general Python
Have permissions to install python packages onto computer
Internet connection

Description
Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language.

In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python.

We'll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files.

Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text.

We'll understand fundamental NLP concepts such as stemming, lemmatization, stop words, phrase matching, tokenization and more!

Next we will cover Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in text to their appropriate part of speech, such as nouns, verbs and adjectives, an essential part of building intelligent language systems.

We'll also learn about named entity recognition, allowing your code to automatically understand concepts like money, time, companies, products, and more simply by supplying the text information.

Through state of the art visualization libraries we will be able view these relationships in real time.

Then we will move on to understanding machine learning with Scikit-Learn to conduct text classification, such as automatically building machine learning systems that can determine positive versus negative movie reviews, or spam versus legitimate email messages.

We will expand this knowledge to more complex unsupervised learning methods for natural language processing, such as topic modelling, where our machine learning models will detect topics and major concepts from raw text files.

This course even covers advanced topics, such as sentiment analysis of text with the NLTK library, and creating semantic word vectors with the Word2Vec algorithm.

Included in this course is an entire section devoted to state of the art advanced topics, such as using deep learning to build out our own chat bots!

Not only do you get fantastic technical content with this course, but you will also get access to both our course related Question and Answer forums, as well as our live student chat channel, so you can team up with other students for projects, or get help on the course content from myself and the course teaching assistants.

All of this comes with a 30 day money back garuantee, so you can try the course risk free.

What are you waiting for? Become an expert in natural language processing today!

I will see you inside the course,

Jose

Who this course is for:
Python developers interested in learning how to use Natural Language Processing.
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NLP - Natural Language Processing with Python

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Big Data Parallel Processing by SimpleIT

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Big data: Parallel Processing by SimpleIT

Big Data Parallel Processing by SimpleIT
Video: .MP4, 1280x720 | Audio: AAC, 44kHz, 2ch | Duration: 30m
Genre: eLearning | Language: English | Size: 200 MB

This course was created for people at various levels of expertise (both technical and non-technical) to help them realize what parallel processing is, what are its main strengths and limitations.

This class, like all SimpleIT classes, uses simple examples to illustrate how simple it could be to understand IT. I hope it will turn on your imagination and result in new thoughts surrounding 'parallel processing'.

See you on my class!

What you'll learn
Explain how parallel processing works
List main benefits of using parallel processing including its limitations
Understand where and how parallel processing is already used in surrounding world
Are there any course requirements or prerequisites?
Be able to absorb new information
Curious of modern computing technology (no knowledge required)
Who this course is for:
All curious people (non-technical should understand as well)
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Post Processing Video 20

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Post Processing Video 20
Post Processing Video 20
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour | 1.04 GB
Genre: eLearning | Language: English

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Georgy Chernyadyev Post Processing Video 64

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Georgy Chernyadyev Post Processing Video 64
Georgy Chernyadyev Post Processing Video 64
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 45M | 553 MB
Genre: eLearning | Language: English

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Alteryx - Data processing, Data Manipulation and Analytics

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Alteryx - Data processing, Data Manipulation and Analytics
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 4.5 hour | Size: 3.34 GB
A comprehensive guide to ETL annd Data Analytics for BI/ETL Developers and Data Scientists

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Georgy Chernyadyev Post Processing Video 63

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Georgy Chernyadyev Post Processing Video 63
Georgy Chernyadyev Post Processing Video 63
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 44M | 482 MB
Genre: eLearning | Language: English

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Natural Language Processing (LiveLessons)

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Natural Language Processing (LiveLessons)
Natural Language Processing (LiveLessons)
MP4 | Video: h264, 1280x720 | Audio: AAC, 48 KHz, 2 Ch
Genre: eLearning | Language: English
Duration: 2h 21m | 4.12 GB


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