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)
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Further reading

  • Minsky M. L., Papert S. A., Perceptrons, The MIT Press, 1969
  • Ramachandran P., Zoph P., Le V. L., Searching for Activation Functions, arXiv:1710.05941 [cs.NE]
  • Rumelhart D. E., Hinton G. E., Williams R. J., Learning representations by back-propagating errors, Nature 323, 1986
  • Glorot X., Bengio Y., Understanding the difficulty of training deep feedforward neural networks, Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, 2010
  • He K., Zhang X., Ren S., Sun J., Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, arXiv:1502.01852 [cs.CV]
  • Holdroyd T., TensorFlow 2.0 Quick Start Guide, Packt Publishing, 2019
  • Kingma D. P., Ba J., Adam: A Method for Stochastic Optimization, arXiv:1412.6980 [cs.LG]
  • Duchi J., Hazan E., Singer Y., Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, Journal of Machine Learning Research 12, 2011
  • Zeiler M...