Deep Learning With r

Applied Machine Learning and Deep Learning with R  eBooks & eLearning

Posted by naag at Nov. 7, 2017
Applied Machine Learning and Deep Learning with R

Applied Machine Learning and Deep Learning with R
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2 Hours 15M | 575 MB
Genre: eLearning | Language: English

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

Deep Learning with R  eBooks & eLearning

Posted by naag at March 29, 2017
Deep Learning with R

Deep Learning with R
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 4 Hours | 649 MB
Genre: eLearning | Language: English

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.

Deep Learning with Keras  eBooks & eLearning

Posted by readerXXI at Feb. 20, 2018
Deep Learning with Keras

Deep Learning with Keras
by Antonio Gulli and Sujit Pal
English | 2017 | ISBN: 1787128423 | 310 Pages | True PDF/Code Files | 18/0.1 MB

Deep Learning with TensorFlow: Explore neural networks with Python  eBooks & eLearning

Posted by First1 at Nov. 5, 2017
Deep Learning with TensorFlow: Explore neural networks with Python

Deep Learning with TensorFlow: Explore neural networks with Python by Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
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

Deep Learning with Keras  eBooks & eLearning

Posted by naag at Feb. 5, 2018
Deep Learning with Keras

Deep Learning with Keras
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | 356 MB
Genre: eLearning | Language: English

Advanced Deep Learning with Keras  eBooks & eLearning

Posted by naag at Dec. 31, 2017
Advanced Deep Learning with Keras

Advanced Deep Learning with Keras
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 5 Hours 11M | 758 MB
Genre: eLearning | Language: English

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.

Deep Learning with Python  eBooks & eLearning

Posted by tarantoga at Dec. 26, 2017
Deep Learning with Python

François Chollet, "Deep Learning with Python"
ISBN: 1617294438 | 2017 | EPUB/PDF | 384 pages | 8 MB/11 MB

Practical Machine Learning with R and Python: Machine Learning in Stereo  eBooks & eLearning

Posted by AlenMiler at Dec. 10, 2017
Practical Machine Learning with R and Python: Machine Learning in Stereo

Practical Machine Learning with R and Python: Machine Learning in Stereo by Tinniam V Ganesh
English | 1 Dec. 2017 | ISBN: 1973443503 | ASIN: B077WFS87Z | 246 Pages | AZW3 | 2.24 MB
Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python

Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python By Santanu Pattanayak
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.

Machine Learning with R  eBooks & eLearning

Posted by AvaxGenius at Nov. 25, 2017
Machine Learning with R

Machine Learning with R By Abhijit Ghatak
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.