Text-book of Advanced Machine Work PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Text-book of Advanced Machine Work PDF full book. Access full book title Text-book of Advanced Machine Work by Robert Henry Smith. Download full books in PDF and EPUB format.
Author: Robert Henry Smith
Publisher:
ISBN:
Category : Machine-shop practice
Languages : en
Pages : 760
Get Book Here
Book Description
Author: Robert Henry Smith
Publisher:
ISBN:
Category : Machine-shop practice
Languages : en
Pages : 760
Get Book Here
Book Description
Author: Kevin P. Murphy
Publisher: MIT Press
ISBN: 0262369303
Category : Computers
Languages : en
Pages : 858
Get Book Here
Book Description
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
Author: James A. Harvey
Publisher: Industrial Press Inc.
ISBN: 0831132272
Category : Technology & Engineering
Languages : en
Pages : 315
Get Book Here
Book Description
Written by an experienced machinist and plastic injection mold maker, this groundbreaking manual will have users thinking and producing like experienced machinists. it provides practical "how-to" information that can immediately be used to improve one's machining skills, craftsmanship, and productivity.
Author: Robert H 1852-1916 Smith
Publisher: Legare Street Press
ISBN: 9781016229739
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the "public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Author: John Hearty
Publisher: Packt Publishing Ltd
ISBN: 1784393835
Category : Computers
Languages : en
Pages : 278
Get Book Here
Book Description
Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.
Author:
Publisher:
ISBN: 1108575544
Category :
Languages : en
Pages : 598
Get Book Here
Book Description
Author: Robert Henry Smith
Publisher:
ISBN:
Category : Machine-shop practice
Languages : en
Pages : 434
Get Book Here
Book Description
Author: Massachusetts Institute of Technology
Publisher:
ISBN:
Category :
Languages : en
Pages : 310
Get Book Here
Book Description
Author: Pratt Institute. Library
Publisher:
ISBN:
Category : Best books
Languages : en
Pages : 36
Get Book Here
Book Description
Author: Pratt Institute. Library
Publisher:
ISBN:
Category : Best books
Languages : en
Pages : 550
Get Book Here
Book Description
