Deep Learning: Recurrent Neural Networks with Python [Video]

Book Image

Deep Learning: Recurrent Neural Networks with Python [Video]

By : AI Sciences

Book Image

Deep Learning: Recurrent Neural Networks with Python [Video]

By: AI Sciences

Overview of this book

With the exponential growth of user-generated data, there is a strong need to move beyond standard neural networks in order to perform tasks such as classification and prediction. Here, architectures such as RNNs, Gated Recurrent Units (GRUs), and Long Short Term Memory (LSTM) are the go-to options. Hence, for any deep learning engineer, mastering RNNs is a top priority. This course begins with the basics and will gradually equip you with not only the theoretical know-how but also the practical skills required to successfully build, train, and implement RNNs. This course contains several exercises on topics such as gradient descents in RNNs, GRUs, LSTM, and so on. This course also introduces you to implementing RNNs using TensorFlow. The course culminates in two exciting and realistic projects: creating an automatic book writer and a stock price prediction application. By the end of this course, you will be equipped with all the skills required to confidently use and implement RNNs in your applications. The code bundle for this course is available at https://github.com/AISCIENCES/mastering_recurrent_neural_networks

Free Chapter

Introduction

Introduction to AI Sciences

Focus of the Course

Applications of RNN

Human Activity Recognition

Image Captioning

Machine Translation

Speech Recognition

Stock Price Predictions

When to Model RNN

Deep Neural Network (DNN) Overview

PyTorch Installation and Tensors Introduction

Automatic Differentiation PyTorch New

Why DNNs in Machine Learning

Representational Power and Data Utilization Capacity of DNN

Perceptron Exercise

Perceptron Exercise Solution

Perceptron Implementation

DNN Architecture

DNN Architecture Exercise

DNN Architecture Exercise Solution

DNN Forward Step Implementation

DNN Why Activation Function Is Required

DNN Why Activation Function Is Required Exercise

DNN Why Activation Function Is Required Exercise Solution

DNN Properties of Activation Function

DNN Activation Functions in PyTorch

DNN What Is Loss Function

DNN What Is Loss Function Exercise

DNN What Is Loss Function Exercise Solution

DNN What Is Loss Function Exercise 02

DNN What Is Loss Function Exercise 02 Solution

DNN Loss Function in PyTorch

DNN Gradient Descent

DNN Gradient Descent Exercise

DNN Gradient Descent Exercise Solution

DNN Gradient Descent Implementation

DNN Gradient Descent Stochastic Batch Minibatch

DNN Gradient Descent Summary

DNN Implementation Gradient Step

DNN Implementation Stochastic Gradient Descent

DNN Implementation Batch Gradient Descent

DNN Implementation Minibatch Gradient Descent

DNN Implementation in PyTorch

DNN Weights Initializations

DNN Learning Rate

DNN Batch Normalization

DNN Batch Normalization Implementation

DNN Optimizations

DNN Dropout in PyTorch

DNN Early Stopping

DNN Hyperparameters

DNN PyTorch CIFAR10 Example

RNN Architecture

Introduction to Module

Fixed Length Memory Model

Fixed Length Memory Model Exercise

Fixed Length Memory Model Exercise Solution Part 01

Fixed Length Memory Model Exercise Solution Part 02

Infinite Memory Architecture

Infinite Memory Architecture Exercise

Infinite Memory Architecture Solution

ManyToMany Model

ManyToMany Model Exercise 01

ManyToMany Model Solution 01

ManyToMany Model Exercise 02

ManyToMany Model Solution 02

ManyToOne Model

OneToMany Model Exercise

OneToMany Model Solution

OneToMany Model

ManyToOne Model Exercise

ManyToOne Model Solution

Activity Many to One

Activity Many to One Exercise

Activity Many to One Solution

ManyToMany Different Sizes Model

Activity Many to Many Nmt

Deep RNNs Exercise

Deep RNNs Solution

Gradient Descent in RNN

Introduction to Gradient Descent Module

Equations Exercise

Equations Solution

Why Gradients Exercise

Why Gradients Solution

Chain Rule in Action

Back Propagation Through Time

RNN Implementation

Automatic Differentiation

Automatic Differentiation PyTorch

Language Modeling Next Word Prediction Vocabulary Index

Language Modeling Next Word Prediction Vocabulary Index Embeddings

Language Modeling Next Word Prediction RNN Architecture

Language Modeling Next Word Prediction Python 1

Language Modeling Next Word Prediction Python 2

Language Modeling Next Word Prediction Python 3

Language Modeling Next Word Prediction Python 4

Language Modeling Next Word Prediction Python 5

Language Modeling Next Word Prediction Python 6

Sentiment Classification Using RNN

Vocabulary Implementation

Vocabulary Implementation Helpers

Vocabulary Implementation from File

Vanishing Gradients in RNN

Introduction to a Better RNN Module

Introduction to Vanishing Gradients in RNN

Gated Recurrent Unit (GRU)

Gated Recurrent Unit (GRU) Equations

Long Short Term Memory (LSTM)

Long Short Term Memory (LSTM) Equations

Bidirectional Recurrent Neural Networks

Attention Model

Attention Model Equation

TensorFlow

TensorFlow Text Classification Example using RNNs

Project 1: Book Writer

Modelling RNN Architecture

Modelling RNN Model in TensorFlow

Modelling RNN Model Training

Modelling RNN Model Text Generation

Project 2: Stock Price Prediction

Problem Statement

Data Preparation

RNN Model Training and Evaluation

Further Reading and Resources

Further Reading and Resources

Customer Reviews

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Chapter 3

Deep Neural Network (DNN) Overview

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Section 14

DNN Why Activation Function Is Required

This video explains why the activation function is required in DNN.