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Build smart language applications with the cutting-edge field of Deep Learning with PyTorch

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 Sennt 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 Sennt Analyzer that analyzes data to detee 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 ee, 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 eeering 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 Sennt Analysis with Bag-of-Words

Lecture 34 Sennt 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 Sennt 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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