Applied Machine Learning From Theory To Deployment
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Applied Machine Learning: From Theory to Deployment
Author: Dr. Nagesh S. Salimath
language: en
Publisher: Xoffencer International Book Publication House
Release Date: 2025-03-05
Numerous sectors have been revolutionized machine learning (ML), which has made it possible to make decisions based on data and to automate processes. This book examines the whole machine learning pipeline, beginning with theoretical underpinnings and ending with implementation in the actual world. In this section, we discuss fundamental algorithms, methodologies for training models, assessment methods, and optimization strategies. There includes a comprehensive discussion on practical elements such as the preparation of data, the engineering of features, and the monitoring of hyperparameters. In addition, we examine the problems that pertain to the deployment of machine learning models, which include scalability, interpretability, and ethical considerations. Readers will be equipped with the abilities necessary to construct, assess, and deploy solid machine learning solutions in a variety of domains by reading this book, which bridges the gap between theory and actual application.
Deep Learning for Coders with fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Applied Machine Learning
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical under¬pinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent data, select and tune algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a non-rigorous mathematical style, the book covers a broad array of machine learning topics with special emphasis on methods that have been profitably employed. Coverage includes: •Supervised learning•Statistical learning•Learning with support vector machines (SVM)•Learning with neural networks (NN)•Fuzzy inference systems•Data clustering•Data transformations•Decision tree learning•Business intelligence•Data mining•And much more