Book Image

Hands-On Deep Learning Algorithms with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Deep Learning Algorithms with Python

By: Sudharsan Ravichandiran

Overview of this book

Deep learning is one of the most popular domains in the AI space that allows you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithms—from basic to advanced—and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles involved, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into recurrent neural networks (RNNs) and LSTM and how to generate song lyrics with RNN. Next, you will master the math necessary to work with convolutional and capsule networks, widely used for image recognition tasks. You will also learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Finally, you will explore GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: Getting Started with Deep Learning
4
Section 2: Fundamental Deep Learning Algorithms
10
Section 3: Advanced Deep Learning Algorithms

Doc2vec

So far, we have seen how to generate embeddings for a word. But how can we generate the embeddings for a document? A naive method would be to compute a word vector for each word in the document and take an average of it. Mikilow and Le introduced a new method for generating the embeddings for documents instead of just taking the average of word embeddings. They introduced two new methods, called PV-DM and PV-DBOW. Both of these methods just add a new vector, called paragraph id. Let's see how exactly these two methods work.

Paragraph Vector – Distributed Memory model

PV-DM is similar to the CBOW model, where we try to predict the target word given a context word. In PV-DM, along with word vectors, we introduce...