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Neural Networks with Keras Cookbook

By : V Kishore Ayyadevara

3.3 (8)

Buy this Book

Neural Networks with Keras Cookbook

3.3 (8)

By: V Kishore Ayyadevara

Buy this Book

Overview of this book

This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data. Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks. We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems. Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game. By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.

Preface

Who this book is for

What this book covers

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Sections

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Free Chapter

Building a Feedforward Neural Network

Introduction

Architecture of a simple neural network

Applications of a neural network

Feed-forward propagation from scratch in Python

Building back-propagation from scratch in Python

Building a neural network in Keras

Building a Deep Feedforward Neural Network

Training a vanilla neural network

Scaling the input dataset

Impact on training when the majority of inputs are greater than zero

Impact of batch size on model accuracy

Building a deep neural network to improve network accuracy

Varying the learning rate to improve network accuracy

Varying the loss optimizer to improve network accuracy

Understanding the scenario of overfitting

Speeding up the training process using batch normalization

Applications of Deep Feedforward Neural Networks

Introduction

Predicting credit default

Assigning weights for classes

Predicting house prices

Categorizing news articles into topics

Classifying common audio

Stock price prediction

Leveraging a functional API

Defining weights for rows

Building a Deep Convolutional Neural Network

Introduction

Inaccuracy of traditional neural networks when images are translated

Building a CNN from scratch using Python

CNNs to improve accuracy in the case of image translation

Gender classification using CNNs

Data augmentation to improve network accuracy

Transfer Learning

Gender classification of the person in an image using CNNs

Gender classification of the person in image using the VGG16 architecture-based model

Visualizing the output of the intermediate layers of a neural network

Gender classification of the person in image using the VGG19 architecture-based model

Gender classification using the Inception v3 architecture-based model

Gender classification of the person in image using the ResNet 50 architecture-based model

Detecting the key points within image of a face

Detecting and Localizing Objects in Images

Introduction

Creating the dataset for a bounding box

Generating region proposals within an image, using selective search

Calculating an intersection over a union between two images

Detecting objects, using region proposal-based CNN

Performing non-max suppression

Detecting a person using an anchor box-based algorithm

Image Analysis Applications in Self-Driving Cars

Traffic sign identification

Predicting the angle within which a car needs to be turned

Instance segmentation using the U-net architecture

Semantic segmentation of objects in an image

Image Generation

Introduction

Generating images that can fool a neural network using adversarial attack

DeepDream algorithm to generate images

Neural style transfer between images

Generating images of digits using Generative Adversarial Networks

Generating images using a Deep Convolutional GAN

Face generation using a Deep Convolutional GAN

Face transition from one to another

Performing vector arithmetic on generated images

Encoding Inputs

Introduction

Need for encoding

Encoding an image

Encoding for recommender systems

Text Analysis Using Word Vectors

Introduction

Building a word vector from scratch in Python

Building a word vector using the skip-gram and CBOW models

Performing vector arithmetic using pre-trained word vectors

Creating a document vector

Building word vectors using fastText

Building word vectors using GloVe

Building sentiment classification using word vectors

Building a Recurrent Neural Network

Introduction

Building an RNN from scratch in Python

Implementing RNN for sentiment classification

Building a LSTM Network from scratch in Python

Implementing LSTM for sentiment classification

Implementing stacked LSTM for sentiment classification

Applications of a Many-to-One Architecture RNN

Generating text

Movie recommendations

Topic-modeling, using embeddings

Forecasting the value of a stock's price

Sequence-to-Sequence Learning

Introduction

Returning sequences of outputs from a network

Building a chatbot

Machine translation

Encoder decoder architecture for machine translation

Encoder decoder architecture with attention for machine translation

End-to-End Learning

Introduction

Connectionist temporal classification (CTC)

Handwritten-text recognition

Image caption generation

Generating captions, using beam search

Audio Analysis

Classifying a song by genre

Generating music using deep learning

Transcribing audio into text

Reinforcement Learning

The optimal action to take in a simulated game with a non-negative reward

The optimal action to take in a state in a simulated game

Q-learning to maximize rewards when playing Frozen Lake

Deep Q-learning to balance a cart pole

Deep Q-learning to play Space Invaders game

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Building word vectors using fastText

fastText is a library created by the Facebook Research Team for the efficient learning of word representations and sentence classification.

fastText differs from word2vec in the sense that word2vec treats every single word as the smallest unit whose vector representation is to be found, but fastText assumes a word to be formed by a n-grams of character; for example, sunny is composed of [sun, sunn, sunny],[sunny, unny, nny], and so on, where we see a subset of the original word of size n, where n could range from 1 to the length of the original word.

Another reason for the use of fastText would be that the words do not meet the minimum frequency cut-off in the skip-gram or CBOW models. For example, the word appended would not be very different than append. However, if append occurs frequently, and in the new sentence we have the word appended...