- Discriminative models learn to find the decision boundary that separates the classes in an optimal way, while generative models learn about the characteristics of each class. That is, discriminative models predict the labels conditioned on the input,
, whereas generative models learn the joint probability distribution, 
. - The generator learns the distribution of images in our dataset. It learns the distribution of handwritten digits in our training set. We feed random noise to the generator and it will convert the random noise into a new handwritten digit similar to the one in our training set.
- The goal of the discriminator is to perform a classification task. Given an image, it classifies it as real or fake; that is, whether the image is from the training set or the one generated by the generator.
-
The loss function for the discriminator...
Hands-On Deep Learning Algorithms with Python
By :
Hands-On Deep Learning Algorithms with Python
By:
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)
Preface
Free Chapter
Section 1: Getting Started with Deep Learning
Introduction to Deep Learning
Getting to Know TensorFlow
Section 2: Fundamental Deep Learning Algorithms
Gradient Descent and Its Variants
Generating Song Lyrics Using RNN
Improvements to the RNN
Demystifying Convolutional Networks
Learning Text Representations
Section 3: Advanced Deep Learning Algorithms
Generating Images Using GANs
Learning More about GANs
Reconstructing Inputs Using Autoencoders
Exploring Few-Shot Learning Algorithms
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