Machine Learning: A Quantitative Approach

Notes (as of May 13, 2019): 

• This text has two formats of black & while and full color editions. Click on See all 2 formats and editions to check out both editions. It is recommended that you make sure you order only from Amazon.com, which is the only source that has all latest updates.
• While the full color edition has a much higher manufacturing cost, it offers much better visual effects for all graphs and a much better learning experience overall. 
• For the black & white edition, all colored figures are downloadable from this book's website.
• Solutions to exercises are provided to help you self-check your self-paced learning.

  Product Description:  Machine learning is a newly-reinvigorated field. It promises to foster many technological advances that may improve the quality of our life significantly, from the use of latest, popular, high-gear gadgets such as smart phones, home devices, TVs, game consoles and even self-driving cars, and so on, to even more fun social and shopping experiences. Of course, for all of us in the circles of high education, academic research and various industrial fields, it offers more challenges and more opportunities.  Whether you are a CS student taking a machine learning class or targeting a machine learning degree, or a scientist or an engineer entering the field of machine learning, this text helps you get up to speed with machine learning quickly and systematically. By adopting a quantitative approach, you will be able to grasp many of the machine learning core concepts, algorithms, models, methodologies, strategies and best practices within a minimal amount of time. Throughout the text, you will be provided with proper textual explanations and graphical exhibitions, augmented not only with relevant mathematics for its rigor, conciseness, and necessity but also with high quality examples for both conventional ML models and deep learning models.  The text encourages you to take a hands-on approach while grasping all rigorous, necessary mathematical underpinnings behind various machine learning models. Specifically, this text helps you: 

• Understand what problems machine learning can help solve 
• Understand various machine learning models, with the strengths and limitations of each model 
• Understand how various major machine learning algorithms work behind the scene so that you would be able to optimize, tune, and size various models more effectively and efficiently 
• Understand a few state-of-the-art neural network architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders (AEs), and so on 
• More importantly, learn how to train and run practically usable neural deep learning models on macOS and Linux-based instances with GPUs

From this book, you will not only learn how machine learning works but also some of the most popular machine learning/deep learning frameworks such as the sklearn, Caffe and Keras/TensorFlow for doing actual machine learning work. The author's goal is that after you are done with this text, you should be able to start embarking on various serious machine learning projects immediately, either using conventional machine learning models or state-of-the-art deep neural network models.