Hands-On Natural Language Processing with Pytorch

Name: Hands-On Natural Language Processing with Pytorch
Rating: 3.7 (37 reviews)

Build smart language applications using Deep Learning

Created byPackt Publishing

Last updated 2/2019

English

What you'll learn

Processing insightful information from raw data using NLP techniques with PyTorch
Working with PyTorch to take advantage of its maximum speed and flexibility
Traditional and modern NLP methods & tools like NLTK, Spacy, Word2Vec & Gensim
Implementing word embedding model and using it with the Gensim toolkit
Sequence-to-sequence models (used in translation) that read one sequence & produces another
Usage of LSTMs using PyTorch for Sentiment Analysis and how its different from RNNs
Comparing and analysing results using Attention networks to improve your project’s performance

Course content

6 sections • 30 lectures • 2h 22m total length

The Course Overview2:47
This video will give you an overview about the course.
Using Deep Learning in Natural Language Processing2:17
An introductory video. We speak about the NLP and deep learning. An intuition of what we are about to learn.
• Value of natural language processing
• Value of deep learning
• Deep learning over traditional NLP
Functions and Features of PyTorch3:45
Why we want to work with PyTorch as our framework of choice and convincing students why PyTorch is great to learn.
• PyTorch as a multipurpose library
• Appreciate the ease of PyTorch
• Future of PyTorch
Installing and Setting Up PyTorch
In this video, let's have a look at installing and setting up PyTorch and get going with PyTorch.
• Where to get PyTorch?
• How to install PyTorch?
• Check installation
Understanding Sentiment Analysis and NMT4:40
Understand our project statement, the solution, and why the project falls into a given NLP paradigm.
• Introduction to projects
• In-depths of sentiment analyzer
• In-depths of NMT

NLTK and spaCy Installations2:27
Install NLTK, spaCy, and their necessary modules.
• Install NLTK and spaCy
• Install stopword module in NLTK
• Install English model in spacy
Tokenization with NLTK6:11
We’llhave a look at the dataset, see different data loading techniques, and perform tokenization.
• Look at the dataset
• Look at sentence tokenization
• Tokenize the entire dataset
Stop Words5:22
In this video, we will look at the stop words. Here we’ll seek the help of NLTK and spaCy to complete this task.
• Explore NLTK and spaCystopwords
• Define custom stopwords
• Stopword removal on the entire dataset
Lemmatization4:17
In this video, we will see how we perform lemmatization and why we even need to perform it.
• Understand lemmatization
• Perform lemmatization on the entire dataset
• Optimize lemmatization
Pipelines4:05
In this video, we’ll stitch together all the steps and practices we have seen so far so that we have a same streamlined, and maintainable piece of code.
• Understand pipelines
• spaCypipelines
• Custom pipelines

Working with Word Embeddings4:16
Understanding the intuitions of word embeddings.
• Types of word embedding
• Concepts of word embedding
Setting Up and Installing gensim0:49
Installing gensim.
• Testing the installations
Exploring Word Embeddings with gensim3:07
Trying out various features in gensim.
• Writing code in python using gensim
Understanding the Embeddings Created3:41
Understanding conceptual relationships in corpus.
• Understand word similarities
Pretrained Embeddings Using Word2vec3:09
Detailed understanding of working of Word2vec.
• Setting up Word2vec
• Text source of Word2vec
• Importance of pre trained embeddings

Working with Recurrent Neural Network5:09
Understand the working of RNN and explore the representation.
• Types of RNN
• Defining of RNN
• Use cases of RNN
Implementing RNN14:33
Start working with PyTorch and implement the sentiment analyzer.
• Explore the architecture
• Define the model
• Optimizer, Loss Function, Device selection
Results with RNN9:35
Obtaining the sentiments classified by RNN.
• Sorting and Padding data within the batch, and epochs for training
• Preparing Data to be fed into RNN
• Running Train and Evaluation modes on model
Working with LSTM5:32
Discuss the structure of LSTM and advantages of LSTMs over RNN.
• LSTM Cell components
• Bidirectional LSTMs
• Multilayer LSTMs
Implementing LSTM6:07
Adapting to the new architecture.
• Implementing to the new architecture
• Structural changes in responses
• Redefining the parameters
Results with LSTM4:53
Completing our Sentiment Analyzer with LSTM.
• Initializing Model
• Running the Epochs
• Readable live results

Intro to seq2seq2:25
Explaining more complex architectures for NLP, with schematic representation.
• Key components of seq2seq
• Encoders and Decoders
• Use cases
Installations1:07
Additional packages and libraries to be installed.
• Installing Torchtext
• Installing English model
• Installing the German Model
Implementing seq2seq – Encoder8:20
We will obtain the data and perform the preprocessing and write our Encoder class.
• Explore Torchtext
• Fields and Bucket Iterators
• GRU units
Implementing seq2seq – Decoder5:29
We completed the decoder with a discussion on the architecture.
• Understanding the output dimensions after each step
• Pulling out the target token from the decoder
Results with seq2seq7:30
Writing our seq2seq model, to tie together encoder and decoder sections.
• Defining hyper-parameters and model objects.
• Training and Evaluation modes for the model
• Running epochs and saving model

Introduction to Attention Networks3:27
Understand the limitations of seq2seq model, and how attention networks improve the seq2seq model.
• Limitations of seq2seq model
• The Intuition behind the working of Attention
• Features of attention networks
Implementing seq2seq – Encoder7:31
Discuss the changes in architecture and understanding the interaction of attention layer with the encoder and decoder.
• Attention Layer and the Attention Vector
• Defining Attention class
• Tensor transformations for attention
Results with Attention Network6:25
Completing the seq2seq model with attention network.
• Changes in the decoder architecture for attention layer
• Tensor transformations
• Running epochs and training the model
The Way Forward3:19
Thoughts on what the steps should be to improve the skills.
• Improvements for the neural translation machine
• Projects for Classification
• Projects for seq2seq models

Requirements

Some basic Machine learning background & experience in programming with Python is required.

Description

The main goal of this course is to train you to perform complex NLP tasks (and build intelligent language applications) using Deep Learning with PyTorch.

You will build two complete real-world NLP applications throughout the course. The first application is a Sentiment Analyzer that analyzes data to determine whether a review is positive or negative towards a particular movie. You will then create an advanced Neural Translation Machine that is a speech translation engine, using Sequence to Sequence models with the speed and flexibility of PyTorch to translate given text into different languages.

By the end of the course, you will have the skills to build your own real-world NLP models using PyTorch's Deep Learning capabilities.

This course uses Python 3.6, Pytorch 1.0, NLTK 3.3.0, and Spacy 2.0 , while not the latest version available, it provides relevant and informative content for legacy users of PyTorch.

About the Author:

Jibin Mathew is a Tech-Entrepreneur, Artificial Intelligence enthusiast and an active researcher. He has spent several years as a Software Solutions Architect, with a focus on Artificial Intelligence for the past 5 years. He has architected and built various solutions in Artificial Intelligence which includes solutions in Computer Vision, Natural Language Processing/Understanding and Data sciences, pushing the limits of computational performance and model accuracies. He is well versed with concepts in Machine learning and Deep learning and serves a consultant for clients from Retail, Environment, Finance and Health care.

Who this course is for:

If you’re a developer, researcher or aspiring AI data scientist ready to dive deeper into this rapidly growing area of artificial intelligence then this course is for you!

Hands-On Natural Language Processing with Pytorch

What you'll learn

Explore related topics

Course content

Up and Running with PyTorch5 lectures • 13min

Data Cleaning and Preprocessing for Sentiment Analysis5 lectures • 22min

Implement Word Embeddings with gensim5 lectures • 15min

Train RNNs and LSTMs Units for Sentiment Analysis6 lectures • 46min

Build a Neural Machine Translator5 lectures • 25min

Improve the Neural Machine Translation with Attention Networks4 lectures • 21min

Requirements

Description

Who this course is for: