Home eBooks Download › math for machine learning

Math For Machine Learning

Download Math For Machine Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Math For Machine Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Mathematics For Machine Learning

DOWNLOAD eBooks

Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23

Mathematics For Machine Learning written by Marc Peter Deisenroth and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-23 with Computers categories.

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Machine Learning Math All You Need To Know Immediately About Math If You Want Spark In Deep Learning Artificial Intelligent And Machine Learning

DOWNLOAD eBooks

Author : Python School
language : en
Publisher: Python School
Release Date : 2021-05-26

Machine Learning Math All You Need To Know Immediately About Math If You Want Spark In Deep Learning Artificial Intelligent And Machine Learning written by Python School and has been published by Python School this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-26 with categories.

★ 55% OFF for Bookstores! NOW at $36.95 instead of $49.95★ You find out about machine learning form A to Z even if you are a beginner Do you want to spark in the science of XXI century? Do you want to become a recreational scientist in deep learning? If you answer yes to one of these previous questions, then keep reading till the end. Machine learning is an advanced form of data analysis and computation which uses the exceptional processing speed and pattern recognition techniques of computers to find and learn new trends in data. Putting it, it is an artificial-intelligence-inspired technique of programming that allows computers to improve their learning capabilities through the data they are fed, or they can access. The concept behind the technique is consistently to improve and to test, and it will be the key in the bigger technological revolution for the future. It is important for any current or aspiring data scientist to join the growing machine learning community, and contribute a quota to improve technology. This guide will focus on the following items: - Induction and Deduction - Decision Trees - Types of Artificial Intelligence and Machine Learning - Stacked Denoising Autoencoders - Robotics - Reinforcement Learning - Linear Algebra - How Companies Use Big Data to Increase Sales - What Is Supervised Machine Learning - How To Build A Predictive Model - Data Preprocessing with Machine Learning - Machine Learning and Robotics - How AI Is Revolutionizing Industry... AND MORE!!! What are you waiting for? A lot of people think that studying ML and Mathematics is difficult. It's because there are a lot of people that don't know the topic in depth so they can't explain it in easy ways. In this book the items will be described in such an easy way you will be surprised! Buy now if you want to spark in deep learning and know whatever it takes about ML and Math

Hands On Mathematics For Deep Learning

DOWNLOAD eBooks

Author : Jay Dawani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-06-12

Hands On Mathematics For Deep Learning written by Jay Dawani and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-12 with Computers categories.

A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.

Math For Deep Learning

DOWNLOAD eBooks

Author : Ronald T. Kneusel
language : en
Publisher: No Starch Press
Release Date : 2021-12-07

Math For Deep Learning written by Ronald T. Kneusel and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-07 with Computers categories.

Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.

Math And Architectures Of Deep Learning

DOWNLOAD eBooks

Author : Krishnendu Chaudhury
language : en
Publisher: Simon and Schuster
Release Date : 2024-05-21

Math And Architectures Of Deep Learning written by Krishnendu Chaudhury and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-21 with Computers categories.

Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively. Inside Math and Architectures of Deep Learning you will find: Math, theory, and programming principles side by side Linear algebra, vector calculus and multivariate statistics for deep learning The structure of neural networks Implementing deep learning architectures with Python and PyTorch Troubleshooting underperforming models Working code samples in downloadable Jupyter notebooks The mathematical paradigms behind deep learning models typically begin as hard-to-read academic papers that leave engineers in the dark about how those models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you’ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Foreword by Prith Banerjee. About the technology Discover what’s going on inside the black box! To work with deep learning you’ll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systematically through the core mathematical concepts you’ll need as a working data scientist: vector calculus, linear algebra, and Bayesian inference, all from a deep learning perspective. About the book Math and Architectures of Deep Learning teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code. You’ll progress from algebra, calculus, and statistics all the way to state-of-the-art DL architectures taken from the latest research. What's inside The core design principles of neural networks Implementing deep learning with Python and PyTorch Regularizing and optimizing underperforming models About the reader Readers need to know Python and the basics of algebra and calculus. About the author Krishnendu Chaudhury is co-founder and CTO of the AI startup Drishti Technologies. He previously spent a decade each at Google and Adobe. Table of Contents 1 An overview of machine learning and deep learning 2 Vectors, matrices, and tensors in machine learning 3 Classifiers and vector calculus 4 Linear algebraic tools in machine learning 5 Probability distributions in machine learning 6 Bayesian tools for machine learning 7 Function approximation: How neural networks model the world 8 Training neural networks: Forward propagation and backpropagation 9 Loss, optimization, and regularization 10 Convolutions in neural networks 11 Neural networks for image classification and object detection 12 Manifolds, homeomorphism, and neural networks 13 Fully Bayes model parameter estimation 14 Latent space and generative modeling, autoencoders, and variational autoencoders A Appendix

Probability Inequalities

DOWNLOAD eBooks

Author : Zhengyan Lin
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-30

Probability Inequalities written by Zhengyan Lin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-30 with Mathematics categories.

Inequality has become an essential tool in many areas of mathematical research, for example in probability and statistics where it is frequently used in the proofs. "Probability Inequalities" covers inequalities related with events, distribution functions, characteristic functions, moments and random variables (elements) and their sum. The book shall serve as a useful tool and reference for scientists in the areas of probability and statistics, and applied mathematics. Prof. Zhengyan Lin is a fellow of the Institute of Mathematical Statistics and currently a professor at Zhejiang University, Hangzhou, China. He is the prize winner of National Natural Science Award of China in 1997. Prof. Zhidong Bai is a fellow of TWAS and the Institute of Mathematical Statistics; he is a professor at the National University of Singapore and Northeast Normal University, Changchun, China.

Math For Machine Learning

DOWNLOAD eBooks

Author : Richard Han
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-07-12

Math For Machine Learning written by Richard Han and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-12 with categories.

From self-driving cars and recommender systems to speech and face recognition, machine learning is the way of the future. Would you like to learn the mathematics behind machine learning to enter the exciting fields of data science and artificial intelligence? There aren't many resources out there that give simple detailed examples and that walk you through the topics step by step. This book not only explains what kind of math is involved and the confusing notation, it also introduces you directly to the foundational topics in machine learning. This book will get you started in machine learning in a smooth and natural way, preparing you for more advanced topics and dispelling the belief that machine learning is complicated, difficult, and intimidating.

Data Science And Machine Learning

DOWNLOAD eBooks

Author : Dirk P. Kroese
language : en
Publisher: CRC Press
Release Date : 2019-11-20

Data Science And Machine Learning written by Dirk P. Kroese and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-20 with Business & Economics categories.

Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Math For Deep Learning

DOWNLOAD eBooks

Author : Ronald T. Kneusel
language : en
Publisher: No Starch Press
Release Date : 2021-11-23

Machine Learning Math

DOWNLOAD eBooks

Author : ML and AI Academy
language : en
Publisher:
Release Date : 2021-02-14

Machine Learning Math written by ML and AI Academy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-14 with categories.

!! 55% OFF for Bookstores!! NOW at 29,95 instead of 39.95 !! Buy it NOW and let your customers get addicted to this awesome book!

Math For Machine Learning

Recent Posts