A trained convolutional layer is made up of many feature detectors, called filters, that slide over an input image as a moving window. We will talk about what's inside a filter in a moment, but for now it can be a black box. Imagine a single filter that has already been trained. Maybe that filter has been trained to detect edges in images, which you might think of as transitions between dark and light. As it passes over the image, its output represents the presence and location of the feature it detects, which can be useful for a second layer of filters. Extending our thought experiment slightly further, now imagine a single filter, in a second convolutional layer, that has also already been trained. Perhaps this new layer has learned to detect right angles, where two edges that have been found by the previous layer are present. On and on we go; as...
Deep Learning Quick Reference
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Deep Learning Quick Reference
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Overview of this book
Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples.
You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks.
By the end of this book, you will be able to solve real-world problems quickly with deep neural networks.
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Current Title:
Deep Learning Quick Reference
Table of Contents (15 chapters)
Preface
Free Chapter
The Building Blocks of Deep Learning
Using Deep Learning to Solve Regression Problems
Monitoring Network Training Using TensorBoard
Using Deep Learning to Solve Binary Classification Problems
Using Keras to Solve Multiclass Classification Problems
Hyperparameter Optimization
Training a CNN from Scratch
Transfer Learning with Pretrained CNNs
Training an RNN from scratch
Training LSTMs with Word Embeddings from Scratch
Training Seq2Seq Models
Using Deep Reinforcement Learning
Generative Adversarial Networks
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