Applied Deep Learning - 2nd Edition

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Introduction
Frequently Asked Questions
About the Book

Fractals from Gradient Descent

Fractal nature of GD

An Introduction to TensorFlow 2.x and 1.x

Computational Graphs
Computational Graphs - Exercises
Eager Execution
Operator Overloading in TensorFlow

I - Single Neuron

Introduction
Linear Regression with One Neuron
Linear Regression with NumPy
Logistic Regression with One Neuron

II - Fully Connected Networks

Multiclass Classification with Fully Connected Networks
Overfitting Example

III - Regularization Techniques

Regularization Techniques
Regularization Decision Boundaries

IV - Gradient Descent Optimizer

Gradient Descent

V - Advanced Optimizers

Simple example of a custom training loop
Comparison of Optimizers

VI - Hyper-parameter Tuning

Hyperparameter Tuning with the Zalando Dataset

VII - Convolutional Neural Networks (CNN)

The Convolution Operator
Multiclass Classification with Convolutional Neural Networks

VIII - Recurrent Neural Networks

Counting with Recurrent Neural Networks

IX - Metric Analysis

Metric Analysis

IX - Generative Adversarial Networks

GAN Example in TensorFlow

X - Autoencoders

Your First Autoencoder
Anomaly Detection with Autoencoders
Denoising Images with Autoencoders based on Feed-Forward Neural Networks
Denoising Images with Autoencoders based on Convolutional Neural Networks

XI - Variational Autoencoders

Your first variational autoencoder

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By Umberto Michelucci with code editing by Michela Sperti
© Copyright TOELT LLC (2020-2022).