The Free eBook - KDnuggets

If you are interested in a top-down, example-driven book on deep learning, check out the draft of the upcoming Deep Learning for Coders with fastai & PyTorch from fast.ai team.

fast.ai is virtually synonymous with its top-down deep learning courses. You may have also used or heard of their equally high quality deep learning, machine learning, linear algebra, and natural language processing courses.

fast.ai is not, however, solely about their courses and video lectures. It has also been a major protagonist in the development of transfer learning for natural language processing; performed an investigation and evaluation of the research into the use of face masks for suppressing the spread of COVID-19; been a voice at the forefront of applied data ethics.

fast.ai has also developed and refined the fastai library during the development of its courses. This library provides easier API access to a variety of machine learning-related functionality, especially when it comes to neural networks. Much of this aspect of the library sits atop PyTorch, making the creation of neural networks with this lower level library easier and flexible for machine learning coders of all skill levels.

Figure

As a bridge between their courseware and the fastai library which it uses, Jeremey Howard and Sylvain Gugger are working on a book titled Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, which is not yet available. However, the current draft of this book can be accessed solely for the purposes of personal learning via its Github repo.

Let’s be explicit what the repo for this book’s draft holds:

These draft notebooks cover an introduction to deep learning, fastai, and PyTorch. fastai is a layered API for deep learning; for more information, see the fastai paper. Everything in this repo is copyright Jeremy Howard and Sylvain Gugger, 2020 onwards.

These notebooks will be used for a course we’re teaching in San Francisco from March 2020, and will be available as a MOOC from around July 2020. In addition, our plan is that these notebooks will form the basis of this book, which you can pre-order. It will not have the same GPL restrictions that are on this draft.

It should be clear that the book is under development, and that the only reason you should be forking the repo or making copies of the notebooks are for your own personal learning; they are not to be shared or hosted elsewhere at this point.

[The content] is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use.

I reached out to Jeremy Howard, one of the books co-authors, and asked him why people should use this book, and what separates it from the crowd of similar learning resources? Here’s what he said:

The book is unusual in that it’s taught “top down”. We teach almost everything through real examples. As we build out those examples, we go deeper and deeper, and we’ll show you how to make your projects better and better. This means that you’ll be gradually learning all the theoretical foundations you need, in context, in such a way that you’ll see why it matters and how it works. We’ve spent years building tools and teaching methods that make previously complex topics very simple.

The book teaches PyTorch, the fastest growing deep learning library, and fastai, the most popular higher level API for PyTorch. The book can be ordered from here.

#2020 jun tutorials #overviews #deep learning #fast.ai #free ebook #deep learning

What is GEEK

Buddha Community

 The Free eBook - KDnuggets
Tia  Gottlieb

Tia Gottlieb

1595543460

The Free eBook - KDnuggets

We are pleased to announce the second edition of our book Data Mining and Machine Learning: Fundamental Concepts and Algorithms, Second Edition, by Mohammed J. Zaki and Wagner Meira, Jr., published by Cambridge University Press, 2020.

Image

The entire book is available to read online for free and the site includes video lectures and other resources.

New to this edition is an entire part devoted to regression and deep learning.

Description & Features:

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression.

This second edition includes a new part on regression with chapters on linear regression, logistic regression, neural networks, deep learning and regression assessment. The book is self-contained with key mathematical concepts and algorithms. It emphasizes the interplay between the geometric, algebraic and probabilistic views, building the key intuition behind the fundamental methods in data mining and machine learning.

#2020 jul tutorials #overviews #algorithms #data mining #free ebook #algorithms

The Free eBook - KDnuggets

If you are interested in a top-down, example-driven book on deep learning, check out the draft of the upcoming Deep Learning for Coders with fastai & PyTorch from fast.ai team.

fast.ai is virtually synonymous with its top-down deep learning courses. You may have also used or heard of their equally high quality deep learning, machine learning, linear algebra, and natural language processing courses.

fast.ai is not, however, solely about their courses and video lectures. It has also been a major protagonist in the development of transfer learning for natural language processing; performed an investigation and evaluation of the research into the use of face masks for suppressing the spread of COVID-19; been a voice at the forefront of applied data ethics.

fast.ai has also developed and refined the fastai library during the development of its courses. This library provides easier API access to a variety of machine learning-related functionality, especially when it comes to neural networks. Much of this aspect of the library sits atop PyTorch, making the creation of neural networks with this lower level library easier and flexible for machine learning coders of all skill levels.

Figure

As a bridge between their courseware and the fastai library which it uses, Jeremey Howard and Sylvain Gugger are working on a book titled Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, which is not yet available. However, the current draft of this book can be accessed solely for the purposes of personal learning via its Github repo.

Let’s be explicit what the repo for this book’s draft holds:

These draft notebooks cover an introduction to deep learning, fastai, and PyTorch. fastai is a layered API for deep learning; for more information, see the fastai paper. Everything in this repo is copyright Jeremy Howard and Sylvain Gugger, 2020 onwards.

These notebooks will be used for a course we’re teaching in San Francisco from March 2020, and will be available as a MOOC from around July 2020. In addition, our plan is that these notebooks will form the basis of this book, which you can pre-order. It will not have the same GPL restrictions that are on this draft.

It should be clear that the book is under development, and that the only reason you should be forking the repo or making copies of the notebooks are for your own personal learning; they are not to be shared or hosted elsewhere at this point.

[The content] is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use.

I reached out to Jeremy Howard, one of the books co-authors, and asked him why people should use this book, and what separates it from the crowd of similar learning resources? Here’s what he said:

The book is unusual in that it’s taught “top down”. We teach almost everything through real examples. As we build out those examples, we go deeper and deeper, and we’ll show you how to make your projects better and better. This means that you’ll be gradually learning all the theoretical foundations you need, in context, in such a way that you’ll see why it matters and how it works. We’ve spent years building tools and teaching methods that make previously complex topics very simple.

The book teaches PyTorch, the fastest growing deep learning library, and fastai, the most popular higher level API for PyTorch. The book can be ordered from here.

#2020 jun tutorials #overviews #deep learning #fast.ai #free ebook #deep learning

Deep Learning: The Free eBook - KDnuggets

In the past few weeks, KDnuggets has brought a selection of free data science-related ebooks to our readers. This week we will continue this new tradition, and will do so by looking at one of the most influential books in the space of the past five years.

Deep Learning book cover

Deep Learning, by Ian Goodfellow, Yoshua Bengio and Aaron Courville, was originally released in 2016 as one of the first books dedicated to the at-the-time exploding field of deep learning. Not only was it a first, it was also written by a team of standout researchers at the forefront of developments at the time, and has remained a highly -influential and -regarded work in deep neural networks.

This is a bottom-up, theory-heavy treatise on deep learning. This is not a book full of code and corresponding comments, or a surface-level hand wavy overview of neural networks. This is an in-depth mathematics-based explanation of the field.

Like many others who set out to perfect their understanding of deep learning when it was released, this book is a personal favorite of mine. I have never treated it as a book to read cover to cover at a single effort; instead, I find myself reading chapters and selections of chapters over long periods of time. In fact, though I have owned a paper copy of the book since it was first released, I can admit that I have never read the entire book; on the other hand, there are a few chapters I have read more than once.

The book’s table of contents are as follow:

  1. Introduction

Part I: Applied Math and Machine Learning Basics

  1. Linear Algebra
  2. Probability and Information Theory
  3. Numerical Computation
  4. Machine Learning Basics

Part II: Modern Practical Deep Networks

  1. Deep Feedforward Networks
  2. Regularization for Deep Learning
  3. Optimization for Training Deep Models
  4. Convolutional Networks
  5. Sequence Modeling: Recurrent and Recursive Nets
  6. Practical Methodology
  7. Applications

Part III: Deep Learning Research

  1. Linear Factor Models
  2. Autoencoders
  3. Representation Learning
  4. Structured Probabilistic Models for Deep Learning
  5. Monte Carlo Methods
  6. Confronting the Partition Function
  7. Approximate Inference
  8. Deep Generative Models

I actually want to come clean about something at this point: this “ebook” isn’t exactly an ebook at all. While the book exists in full format on the book’s website, their is no single, bundled collection of downloadable chapters of this book available, for contractual reasons between the authors and their publisher. Instead, chapters can be freely read one by one on the website. If this poses a problem for you, or if you find the book worthy enough that you would like a physical copy for future reference, you can always shell out the cash for your very own.

#2020 may tutorials # overviews #aaron courville #book #deep learning #free ebook #ian goodfellow #neural networks #yoshua bengio

Arun A

1623303335

Syncfusion Free Ebooks | Razor Components Succinctly

OVERVIEW
Razor components are specific building blocks within the Blazor framework. They can perform many roles: representing a specific piece of the user interface, a view component, or a tag helper; or representing a layout or an entire page. In Razor Components Succinctly, you will explore how to create and work with both simple and advanced Razor components. Longtime Succinctly author Ed Freitas will show you how to write a basic component using one-way data binding and events, and then two-way data binding, event callbacks, life cycle methods, and component references. Finally, you’ll see how to enable component reuse by creating a component template.

TABLE OF CONTENTS

#razor components #free #ebooks #blazor

Foundations of Data Science: The Free eBook - KDnuggets

As has become tradition on KDnuggets, let’s start a new week with a new eBook. This time we check out a survey style text with a variety of topics, Foundations of Data Science.

We’re back at it with a new free eBook again this week. This time we will be covering a text with a name that speaks for itself, Foundations of Data Science, written by Avrim Blum, John Hopcroft, and Ravindran Kannan. A book with a such a name is making a pretty big statement. Luckily, its content backs it up.

Cover"

First off, it should be noted that this book is not structured like a typical data science book. Neither its chapters nor their progression fit the mold of a standard contemporary data science text in my view. You can see, from the table of contents listed below, that the text really surveys a wide array of disparate topics, as opposed to simply creating an equivalency between data science and machine learning, for example, and progressing as such:

  1. Introduction
  2. High-Dimensional Space
  3. Best-Fit Subspaces and Singular Value Decomposition (SVD)
  4. Random Walks and Markov Chains
  5. Machine Learning
  6. Algorithms for Massive Data Problems: Streaming, Sketching, and Sampling
  7. Clustering
  8. Random Graphs
  9. Topic Models, Nonnegative Matrix Factorization, Hidden Markov Models, and Graphical Models
  10. Other Topics
  11. Wavelets
  12. Appendix

The varied high-level topics, and early inclusion of chapters on high-dimensional space, subspaces, and random walks or Markov Chains, reinforces this survey style. This also makes me think of another classic book in data science with which you may be familiar, Mining of Massive Datasets. Stressing that this text focuses on “foundation,” you won’t find the latest neural network architectures covered herein. However, if you want to eventually be able to understand the whys and hows of some of these more complex approaches to data science problem solving, you should find Foundations of Data Science useful.

Matrix factorization, graph theory, kernel methods, clustering theory, streaming, gradients descent, data sampling; these are all concepts that will serve you well later, when it comes to solving data science problems, and they are all essential building blocks to implementing more complex approaches as well. You won’t be able to understand neural networks without gradient descent. You can’t analyze social media networks without graph theory. The models you build won’t be of value if you can’t understand when and why you would sample from data.

Similar to some other books we have recently profiled (such as The Elements of Statistical Learning and Understanding Machine Learning), this book is unabashedly theoretical. There is no code. There are no Python libraries being leaned on. There is no hand-waviness. There are only thorough explanations leading to understanding of these varied topics, should you spend the necessary time reading.

#2020 jul tutorials #overviews #data science #free ebook #data analysis