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

Deep Convolutional Networks

In this chapter, we continue our pragmatic exploration of the world of deep learning, analyzing deep convolutional networks. Deep convolutional networks represent the most accurate and best performing visual processing technique for almost any purpose. Results like the ones obtained in fields such as real-time image recognition, self-driving cars, and deep reinforcement learning have been possible thanks to the expressivity of this kind of network. Employing this technique together with all the elements discussed in the previous chapters makes it possible to achieve extraordinary results in the fields of video processing, decoding, segmentation, and generation.

In particular, in this chapter, we are going to discuss the following topics:

  • Deep convolutional networks
  • Convolutions, atrous convolutions
  • Separable convolutions, and transpose convolutions
  • Pooling and other support layers

At this point, we can start discussing...