Book Image

Machine Learning Using TensorFlow Cookbook

By : Luca Massaron, Alexia Audevart, Konrad Banachewicz
Book Image

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.
Table of Contents (15 chapters)
5
Boosted Trees
11
Reinforcement Learning with TensorFlow and TF-Agents
13
Other Books You May Enjoy
14
Index

Index

Symbols

10-way softmax 74

A

absolute loss function 39

activation functions 19

implementing 19-22

working with 158-162

Adadelta algorithm

reference link 52

Adagrad algorithm 52

reference link 52

Adult Dataset

reference link 133

advanced CNN

implementing 247-253

AWS SageMaker

reference link 385

Azure ML

reference link 385

B

backpropagation 151

implementing 44-51

working 51

batch training

working 55

working with 53, 54

beam search 299

binary classification problem

approaching, with BoostedTreesClassifier 139-148

binary classification problem, approaching with BoostedTreesClassifier

output 149, 150

C

CartPole problems

solving, with TF-Agents 338-345

Catboost

URL 230

categorical data

processing 211-214

Census dataset 133

CIFAR-10 dataset

reference link 253

classifier

creating, for iris dataset 56-61

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