Book Image

Mastering Machine Learning Algorithms - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
Other Books You May Enjoy

Example of a deep convolutional network with TensorFlow and Keras

In the first example, we want to consider the complete MNIST handwritten digit dataset again, but instead of using an MLP, we are going to employ a small deep convolutional network. The first step consists of loading and normalizing the dataset:

import tensorflow as tf
import numpy as np
(X_train, Y_train), (X_test, Y_test) = \
width = height = X_train.shape[1]
X_train = X_train.reshape(
        (X_train.shape[0], width, height, 1)).\
                  astype(np.float32) / 255.0
X_test = X_test.reshape(
        (X_test.shape[0], width, height, 1)).\
                 astype(np.float32) / 255.0
Y_train = tf.keras.utils.to_categorical(
        Y_train, num_classes=10)
Y_test = tf.keras.utils.to_categorical(
        Y_test, num_classes=10)

We can now define the model architecture. The data points are quite small (28...