Learn Pytorch For Natural Language Processing

What you'll learn
Work with Deep Learning models and architectures including layers, activations, loss functions, gradients, chain rule, forward and backward passes, and optimizers.
Apply Deep Learning architectures to solve Machine Learning problems for Structured Datasets, Computer Vision, and Natural Language Processing.
Utilize the concept of Transfer Learning by using pre-trained Deep Learning models to your own problems.
Implementing the word embedding model and using it with the Gensim toolkit.
Processing insightful information from raw data using NLP techniques with PyTorch.
Comparing and analyzing results using Attention networks to improve your project's performance.
Requirements
Basic knowledge of machine learning concepts and Python programming is required for this course.
Description
PyTorch: written in Python, is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. PyTorch is a Deep Learning framework that is a boon for researchers and data scientists.This course takes a practical approach and is filled with real-world examples to help you create your own application using PyTorch! Learn the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code. Get yourself acquainted with the advanced concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks. Moving further you will build real-world NLP applications such as Sentiment Analyzer & advanced Neural Translation Machine.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, PyTorch Deep Learning in 7 Days is for those who are in a hurry to get started with PyTorch. You will be introduced to the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code. This course is an attempt to break the myth that Deep Learning is complicated and show you that with the right choice of tools combined with a simple and intuitive explanation of core concepts, Deep Learning is as accessible as any other application development technologies out there. It's a journey from diving deep into the fundamentals to getting acquainted with the advanced concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks. By the end of the course, you will be able to build Deep Learning applications with PyTorch.The second course, Hands-On Natural Language Processing 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.About the Authors:Will Ballard is the chief technology officer at GLG, responsible for engineering and IT. He was also responsible for the design and operation of large data centres that helped run site services for customers including Gannett, Hearst Magazines, NFL, NPR, The Washington Post, and Whole Foods. He has also held leadership roles in software development at NetSolve (now Cisco), NetSpend, and Works (now Bank of America).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 as a consultant for clients from Retail, Environment, Finance and Health care.
Overview
Section 1: PyTorch Deep Learning in 7 Days
Lecture 1 The Course overview
Lecture 2 Quick Intro to PyTorch
Lecture 3 Installation and Jupyter Notebook Setup
Lecture 4 Tensors and Basic Tensor Operations
Lecture 5 Advanced Tensor Operations
Lecture 6 Loading and Saving Data
Lecture 7 Assignment
Lecture 8 Introduction to Neural Networks
Lecture 9 Creating a Neural Network with PyTorch Sequential
Lecture 10 Activations, Loss Functions, and Gradients
Lecture 11 Forward and Backward Passes
Lecture 12 Building a Network with nn.Module
Lecture 13 Assignment
Lecture 14 Loading Structured Data for Classification
Lecture 15 Preprocessing Data
Lecture 16 Classification, Accuracy, and the Confusion Matrix
Lecture 17 Loading Structured Data for Regression
Lecture 18 Neural Networks for Regression
Lecture 19 Assignment
Lecture 20 Convolutional Networks for Image Analysis
Lecture 21 Convolutional Concepts: Filters, Strides, Padding, and Pooling
Lecture 22 Implementing a Convolutional Network
Lecture 23 Visualizing Convolutional Network Layers
Lecture 24 Implementing an End-To-End Deep Convolutional Network
Lecture 25 Assignment
Lecture 26 Transfer Learning and Prebuilt Models
Lecture 27 Deep Learning with VGG
Lecture 28 Transfer Learning with VGG
Lecture 29 Transfer Learning with ResNet
Lecture 30 Assignment
Lecture 31 Recurrent Networks, RNN, and LSTM, GRU
Lecture 32 Text Modeling with Bag-of-Words
Lecture 33 Sentiment Analysis with Bag-of-Words
Lecture 34 Sentiment Analysis with Word Embeddings
Lecture 35 Assignment
Lecture 36 Introduction to GANs and DCGANs
Lecture 37 Implementing DCGAN Model with PyTorch
Lecture 38 Training and Evaluating DCGAN on an Image Dataset
Lecture 39 Improving Performance
Lecture 40 Assignment
Section 2: Hands-On Natural Language Processing with Pytorch
Lecture 41 The Course Overview
Lecture 42 Using Deep Learning in Natural Language Processing
Lecture 43 Functions and Features of PyTorch
Lecture 44 Installing and Setting Up PyTorch
Lecture 45 Understanding Sentiment Analysis and NMT
Lecture 46 NLTK and spaCy Installations
Lecture 47 Tokenization with NLTK
Lecture 48 Stop Words
Lecture 49 Lemmatization
Lecture 50 Pipelines
Lecture 51 Working with Word Embeddings
Lecture 52 Setting Up and Installing gensim
Lecture 53 Exploring Word Embeddings with gensim
Lecture 54 Understanding the Embeddings Created
Lecture 55 Pretrained Embeddings Using Word2vec
Lecture 56 Working with Recurrent Neural Network
Lecture 57 Implementing RNN
Lecture 58 Results with RNN
Lecture 59 Working with LSTM
Lecture 60 Implementing LSTM
Lecture 61 Results with LSTM
Lecture 62 Intro to seq2seq
Lecture 63 Installations
Lecture 64 Implementing seq2seq – Encoder
Lecture 65 Implementing seq2seq – Decoder
Lecture 66 Results with seq2seq
Lecture 67 Introduction to Attention Networks
Lecture 68 Implementing seq2seq – Encoder
Lecture 69 Results with Attention Network
Lecture 70 The Way Forward
This course is for software development professionals, machine learning enthusiasts and Data Science professionals who would like to practically implement PyTorch and exploit its unique features in their Deep Learning projects.


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