Machine Learning Using TensorFlow Cookbook

By : Luca Massaron, Alexia Audevart, Konrad Banachewicz

Machine Learning Using TensorFlow Cookbook

By: Luca Massaron, Alexia Audevart, Konrad Banachewicz

Overview of this book

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Getting Started with TensorFlow 2.x

How TensorFlow works

Declaring variables and tensors

Using eager execution

Working with matrices

Declaring operations

Implementing activation functions

Working with data sources

Additional resources

Free Chapter

The TensorFlow Way

Operations using eager execution

Layering nested operations

Working with multiple layers

Implementing loss functions

Implementing backpropagation

Working with batch and stochastic training

Combining everything together

Keras

Introduction

Understanding Keras layers

Using the Keras Sequential API

Using the Keras Functional API

Using the Keras Subclassing API

Using the Keras Preprocessing API

Linear Regression

Learning the TensorFlow way of linear regression

Turning a Keras model into an Estimator

Understanding loss functions in linear regression

Implementing Lasso and Ridge regression

Implementing logistic regression

Resorting to non-linear solutions

Using Wide & Deep models

Boosted Trees

Introduction

Neural Networks

Implementing operational gates

Working with gates and activation functions

Implementing a one-layer neural network

Implementing different layers

Using a multilayer neural network

Improving the predictions of linear models

Learning to play Tic-Tac-Toe

Predicting with Tabular Data

Processing numerical data

Processing dates

Processing categorical data

Processing ordinal data

Processing high-cardinality categorical data

Wrapping up all the processing

Setting up a data generator

Creating custom activations for tabular data

Running a test on a difficult problem

Convolutional Neural Networks

Introduction

Implementing a simple CNN

Implementing an advanced CNN

Retraining existing CNN models

Applying StyleNet and the neural style project

Implementing DeepDream

Recurrent Neural Networks

Text generation

Sentiment classification

Stock price prediction

Open-domain question answering

Summary

Transformers

Text generation

Sentiment analysis

Open-domain question answering

Reinforcement Learning with TensorFlow and TF-Agents

GridWorld

CartPole

MAB

Taking TensorFlow to Production

Visualizing Graphs in TensorBoard

Managing Hyperparameter tuning with TensorBoard's HParams

Implementing unit tests

Using multiple executors

Parallelizing TensorFlow

Saving and restoring a TensorFlow model

Using TensorFlow Serving

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Index

Customer Reviews

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Managing Hyperparameter tuning with TensorBoard's HParams

Tuning hyperparameters in a machine learning project can be a real pain. The process is iterative and can take a long time to test all the hyperparameter combinations. But fortunately, HParams, a TensorBoard plugin, comes to the rescue. It allows testing to find the best combination of hyperparameters.

Getting ready

To illustrate how the HParams plugin works, we will use a sequential model implementation on the MNIST dataset. We'll configure HParams and compare several hyperparameter combinations in order to find the best hyperparameter optimization.

How to do it...

First, we'll load the libraries necessary for the script:

import tensorflow as tf
from tensorboard.plugins.hparams import api as hp
import numpy as np
import datetime

Next, we'll load and prepare the MNIST dataset:

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data...

Machine Learning Using TensorFlow Cookbook

By : Luca Massaron, Alexia Audevart, Konrad Banachewicz

Machine Learning Using TensorFlow Cookbook

By: Luca Massaron, Alexia Audevart, Konrad Banachewicz

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