Posted by **naag** at Nov. 7, 2017

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2 Hours 15M | 575 MB

In this course, we will examine in detail the R software, which is the most popular statistical programming language of recent years.

Posted by **naag** at March 29, 2017

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 4 Hours | 649 MB

Deep learning refers to artificial neural networks that are composed of many layers. Deep learning is a powerful set of techniques for finding accurate information from raw data.

Posted by **readerXXI** at Feb. 20, 2018

English | 2017 | ISBN: 1787128423 | 310 Pages | True PDF/Code Files | 18/0.1 MB

Posted by **First1** at Nov. 5, 2017

English | April 24th, 2017 | ASIN: B01N2BAK7T, ISBN: 1786469782 | 370 Pages | EPUB | 6.20 MB

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide

Posted by **naag** at Feb. 5, 2018

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | 356 MB

Posted by **naag** at Dec. 31, 2017

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 5 Hours 11M | 758 MB

Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deeplearning4j, Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible.

Posted by **tarantoga** at Dec. 26, 2017

ISBN: 1617294438 | 2017 | EPUB/PDF | 384 pages | 8 MB/11 MB

Posted by **AlenMiler** at Dec. 10, 2017

English | 1 Dec. 2017 | ISBN: 1973443503 | ASIN: B077WFS87Z | 246 Pages | AZW3 | 2.24 MB

Posted by **AvaxGenius** at Dec. 7, 2017

English | PDF,EPUB | 2017 | 412 Pages | ISBN : 1484230957 | 22.77 MB

Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own.

Posted by **AvaxGenius** at Nov. 25, 2017

English | PDF,EPUB | 2017 | 224 Pages | ISBN : 9811068070 | 7.02 MB

This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it’s applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning.