Natural Language Processing with Deep Learning in Python

Name: Natural Language Processing with Deep Learning in Python
Brand: Lazy Programmer
Price: 99.00 USD
Availability: Discontinued

4.5 Hours

Deal Price$99.00

Suggested Price$120.00

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40 Lessons (4.5h)

Outline, Review, and Logistical Things
Introduction, Outline, and Review2:42
Where to get the code / data for this course2:00
Word Embeddings and Word2Vec
What is a word embedding?10:00
Using pre-trained word embeddings2:17
Word analogies using word embeddings3:51
TF-IDF and t-SNE experiment12:24
Word2Vec introduction5:07
CBOW2:19
Skip-Gram3:30
Skip-Gram3:30
Negative Sampling7:36
Why do I have 2 word embedding matrices and what do I do with them?1:36
Word2Vec in Code with Numpy (part 1)19:49
Word2Vec in Code with Numpy (part 2)1:53
Converting a sequence of word indexes to a sequence of word vectors3:14
How to update only part of a Theano shared variable5:29
Word2Vec in Code with Theano9:57
Word Embeddings using GLoVe
Recommender systems and matrix factorization tutorial11:08
GLoVe - Global Vectors for Word Representation4:12
GLoVe in Code - Numpy Gradient Descent16:48
GLoVe in Code - Theano Gradient Descent3:50
GLoVe in Code - Alternating Least Squares4:42
Visualizing country analogies with t-SNE4:25
Hyperparameter Challenge2:19
Using Neural Networks to Solve NLP Problems
Parts-of-Speech (POS) Tagging5:00
Parts-of-Speech Tagging Baseline15:18
Parts-of-Speech Tagging Recurrent Neural Network13:05
Parts-of-Speech Tagging Hidden Markov Model (HMM)5:58
Named Entity Recognition (NER)3:01
Named Entity Recognition Baseline5:54
Named Entity Recognition RNN2:19
Hyperparameter Challenge II2:13
Recursive Neural Networks (Tree Neural Networks)
Data Description for Recursive Neural Networks6:52
What are Recursive Neural Networks / Tree Neural Networks (TNNs)?5:41
Building a TNN with Recursion4:47
Trees to Sequences6:39
Recursive Neural Network in Theano18:34
Recursive Neural Tensor Networks6:22
Recursive Neural Network in TensorFlow with Recursion4:12
Appendix
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow17:22

DescriptionInstructorImportant DetailsRelated Products

The Complete Guide on Deriving & Implementing Word2Vec, GLoVe, Word Embeddings & Sentiment Analysis

Lazy ProgrammerThe Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

Description

In this course you'll explore advanced natural language processing - the field of computer science and AI that concerns interactions between computer and human languages. Over the course you'll learn four new NLP architectures and explore classic NLP problems like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. By course's end, you'll have a firm grasp on natural language processing and its many applications.

Access 40 lectures & 4.5 hours of content 24/7
Discover Word2Vec & how it maps words to a vector space
Explore GLoVe's use of matrix factorization & how it contributes to recommendation systems
Learn about recursive neural networks which will help solve the problem of negation in sentiment analysis

Specs

Details & Requirements

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion not included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: advanced, but you must have some knowledge of calculus, linear algebra, probability, Python, Numpy, and be able to write a feedforward neural network in Theano and TensorFlow.
All code for this course is available for download here, in the directory nlp_class2

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Internet required

Terms

Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.

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