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)
26
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27
Index

Transfer learning

We have discussed how deep learning is fundamentally based on black-box models that learn how to associate input patterns to specific classification/regression outcomes. The entire processing pipeline that is often employed to prepare the data for specific detections is absorbed by the complexity of the neural architecture. However, the price to pay for high accuracies is a proportionally large number of training samples. State-of-the-art visual networks are trained with millions of images and, obviously, each of them must be properly labeled. Even if there are many free datasets that can be employed to train several models, many specific scenarios need hard preparatory work that sometimes is very difficult to achieve.

Luckily, deep neural architectures are hierarchical models that learn in a structured way. As we have seen in the examples of deep convolutional networks, the first layers become more and more sensitive to detect low-level features, while the higher...