- Autoencoders are unsupervised learning algorithms. Unlike other algorithms, autoencoders learn to reconstruct the input, that is, an autoencoder takes the input and learns to reproduce the input as an output.
-
We can define our loss function as a difference between the actual input and reconstructed input as follows:
Here,
is the number of training samples. - Convolutional Autoencoder (CAE) that uses a convolutional network instead of a vanilla neural network. In the vanilla autoencoders, encoders and decoders are basically a feedforward network. But in CAEs, they are basically convolutional networks. This means the encoder consists of convolutional layers and the decoder consists of transposed convolutional layers, instead of a raw feedforward network.
- Denoising Autoencoders (DAE) are another small variant of the autoencoder...
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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