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Machine Learning Using TensorFlow Cookbook

Machine Learning Using TensorFlow Cookbook

By : Alexia Audevart, Konrad Banachewicz, Luca Massaron
4.9 (16)
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Machine Learning Using TensorFlow Cookbook

Machine Learning Using TensorFlow Cookbook

4.9 (16)
By: Alexia Audevart, Konrad Banachewicz, Luca Massaron

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)
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5
Boosted Trees
11
Reinforcement Learning with TensorFlow and TF-Agents
13
Other Books You May Enjoy
14
Index

Using the Keras Sequential API

The main goal of Keras is to make it easy to create deep learning models. The Sequential API allows us to create Sequential models, which are a linear stack of layers. Models that are connected layer by layer can solve many problems. To create a Sequential model, we have to create an instance of a Sequential class, create some model layers, and add them to it.

We will go from the creation of our Sequential model to its prediction via the compilation, training, and evaluation steps. By the end of this recipe, you will have a Keras model ready to be deployed in production.

Getting ready

This recipe will cover the main ways of creating a Sequential model and assembling layers to build a model with the Keras Sequential API.

To start, we load TensorFlow and NumPy, as follows:

import tensorflow as tf
from tensorflow import keras
from keras.layers import Dense
import numpy as np

We are ready to proceed with an explanation of how...

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