- The different layers of CNN include convolution, pooling, and fully connected layers.
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We slide over the input matrix with the filter matrix by one pixel and perform the convolution operation. But we can not only slide over the input matrix by one pixel-we can also slide over the input matrix by any number of pixels. The number of pixels we slide over the input matrix by the filter matrix is called stride.
- With the convolution operation, we slide over the input matrix with a filter matrix. But in some cases, the filter does not perfectly fit the input matrix. That is, there exists a situation that when we move our filter matrix by two pixels, it reaches the border and the filter does not fit the input matrix, that is, some part of our filter matrix is outside the input matrix. In this case, we perform padding.
- The pooling layer...
Hands-On Deep Learning Algorithms with Python
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Hands-On Deep Learning Algorithms with Python
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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.
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Current Title:
Hands-On Deep Learning Algorithms with Python
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
Assessments
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