Natural Language Processing with Deep Learning in Python

BY
Udemy

Learn the skills to perform tasks like implementing and deriving word2vec, GloVe, word embeddings, and sentiment analysis with neural networks.

Mode

Online

Fees

₹ 699 2999

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course overview

Natural Language Processing with Deep Learning in Python online certification was created by Lazy Programmer Inc., Artificial Intelligence and Machine Learning Engineer, Lazy Programmer Team, and is offered through Udemy Inc. which is an online learning platform, that helps individuals progress in their careers.

Natural Language Processing with Deep Learning in Python online course will teach how the GloVe method uses matrix factorization to find word vectors, using recurrent neural networks to solve natural language processing problems such as part-of-speech tagging and named entity recognition, as well as gain an understanding of how neural networks solve almost any problem, and how to use recursive neural networks to solve the sentiment analysis negation problem

Natural Language Processing with Deep Learning in Python online training is designed for individuals who are familiar with programming and have some experience in it as this course comes with prerequisites such as calculus, matrix addition, multiplication, probability, python coding, Numpy coding, neural networks and backpropagation, gradient descent algorithms, coding feedforward neural network in Theano or TensorFlow, coding recurrent neural network / LSTM / GRU in Theano or TensorFlow, tree algorithms. The interested learners could enrol on the course by paying the fee. 

The highlights

  • Certificate of completion
  • Self-paced course
  • English videos with multi-language subtitles
  • 12 hours of pre-recorded video content
  • 30-day money-back guarantee
  • Unlimited access
  • Accessible on mobile devices and TV

Program offerings

  • Certificate of completion
  • Self-paced course
  • English videos with multi-language subtitles
  • 12 hours of pre-recorded video content
  • 30-day money-back guarantee
  • Unlimited access
  • Accessible on mobile devices and tv

Course and certificate fees

Fees information
₹ 699  ₹2,999
certificate availability

Yes

certificate providing authority

Udemy

Who it is for

What you will learn

Natural language processing Knowledge of python Knowledge of deep learning

After completing the Natural Language Processing with Deep Learning in Python certification course, learners will be able to understand and develop the skills to work in word2vec which include implementation, CBOW method, skip-gram method, and negative sampling optimization. Learners will understand GloVe implementation using gradient descent and alternating least square method, implementation of recursive neural network and neural tensor networks for sentiment analysis, using neural networks for parts-of-speech tagging, and named entity recognition.

The syllabus

Outline, Review and Logistical Things

  • Introduction, Outline and Review
  • How to Succeed in this Course
  • Where to get the code / data for this course
  • Preprocessed Wikipedia Data
  • How to Open Files for Windows Users

Beginner's Corner: Working with Word Vectors

  • What are Vectors?
  • What is a word analogy?
  • Trying to find and assess word vectors using TF-IDF and t-SNE
  • Pretrained word vectors from GloVe
  • Pretrained word vectors from word2vec
  • Text Classification with word vectors
  • Text Classification in Code
  • Using pretrained vectors later in the course
  • Suggestion Box

Review of Language Modeling and Neural Networks

  • Review Section Intro
  • Bigrams and Language Models
  • Bigrams in Code
  • Neural Bigram Model
  • Neural Bigram Model in Code
  • Neural Network Bigram Model
  • Neural Network Bigram Model in Code
  • Improving Efficiency
  • Improving Efficiency in Code
  • Review Section Summary

Word Embeddings and Word2Vec

  • Return of the Bigram
  • CBOW
  • Skip-Gram
  • Hierarchical Softmax
  • Negative Sampling
  • Negative Sampling - Important Details
  • Why do I have 2 word embedding matrices and what do I do with them?
  • Word2Vec implementation tricks
  • Word2Vec implementation outline
  • Word2Vec in Code with Numpy
  • Tensorflow or Theano - Your Choice!
  • Word2Vec Tensorflow Implementation Details
  • Word2Vec Tensorflow in Code
  • Alternative to Wikipedia Data: Brown Corpus

Word Embeddings using GloVe

  • GloVe Section Introduction
  • Matrix Factorization for Recommender Systems - Basic Concepts
  • Matrix Factorization Training
  • Expanding the Matrix Factorization Model
  • Regularization for Matrix Factorization
  • GloVe - Global Vectors for Word Representation
  • Recap of ways to train GloVe
  • GloVe in Code - Numpy Gradient Descent
  • GloVe in Code - Alternating Least Squares
  • GloVe in Tensorflow with Gradient Descent
  • Visualizing country analogies with t-SNE
  • Hyperparameter Challenge
  • Training GloVe with SVD (Singular Value Decomposition)

Unifying Word2Vec and GloVe

  • Pointwise Mutual Information - Word2Vec as Matrix Factorization
  • PMI in Code

Using Neutral Networks to Solve to Solve NLP Problems

  • Parts-of-Speech (POS) Tagging
  • How can neural networks be used to solve POS tagging?
  • Parts-of-Speech Tagging Baseline
  • Parts-of-Speech Tagging Recurrent Neural Network in Theano
  • Parts-of-Speech Tagging Recurrent Neural Network in Tensorflow
  • How does an HMM solve POS tagging?
  • Parts-of-Speech Tagging Hidden Markov Model (HMM)
  • Named Entity Recognition (NER)
  • Comparing NER and POS tagging
  • Named Entity Recognition Baseline
  • Named Entity Recognition RNN in Theano
  • Named Entity Recognition RNN in Tensorflow
  • Hyperparameter Challenge II

Recursive Neutral Networks (Tree Neural Networks)

  • Recursive Neural Networks Section Introduction
  • Sentences as Trees
  • Data Description for Recursive Neural Networks
  • What are Recursive Neural Networks / Tree Neural Networks (TNNs)?
  • Building a TNN with Recursion
  • Trees to Sequences
  • Recursive Neural Tensor Networks
  • RNTN in Tensorflow (Tips)
  • RNTN in Tensorflow (Code)
  • Recursive Neural Network in TensorFlow with Recursion

Theano and Tenser Flow Basics Review

  • (Review) Theano Basics
  • (Review) Theano Neural Network in Code
  • (Review) Tensorflow Basics
  • (Review) Tensorflow Neural Network in Code

Setting up your Environment

  • Anaconda Environment Setup
  • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Extra Help with Python Coding for Beginners

  • How to install wp2txt or WikiExtractor.py
  • How to Uncompress a .tar.gz file
  • How to Code by Yourself (part 1)
  • How to Code by Yourself (part 2)
  • Proof that using Jupyter Notebook is the same as not using it
  • Python 2 vs Python 3
  • Is Theano Dead?

Effective Learning Strategies for Machine Learning

  • How to Succeed in this Course (Long Version)
  • Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
  • Machine Learning and AI Prerequisite Roadmap (pt 1)
  • Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix

  • What is the Appendix?
  • BONUS

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