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Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow By LazyProBgrammer EPUB

Download Book : Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow By LazyProgrammer EPUB


Informations about the book:

Title: Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python

Author: LazyProgrammer

Size: 203 KB

FormatEPUB

Year2019

Pages253

Book Contents:


Chapter 1: What is a neural network?
Chapter 2: Biological analogies
Chapter 3: Getting output from a neural network
Chapter 4: Training a neural network with backpropagation
Chapter 5: Theano
Chapter 6: TensorFlow
Chapter 7: Improving backpropagation with modern techniques - momentum, adaptive learning rate, and regularization
Chapter 8: Unsupervised learning, autoencoders, restricted Boltzmann machines, convolutional neural networks, and LSTMs
Chapter 9: You know more than you think you know


Description:

Profound learning is causing a ripple effect. At the hour of this composition (March 2016), Google's AlghaGo program simply beat 9-dan expert Go player Lee Sedol at the round of Go, a Chinese tabletop game. 
Specialists in the field of Artificial Intelligence thought we were 10 years from accomplishing a triumph against a top proficient Go player, however progress appears to have quickened! 
While profound learning is an intricate subject, it isn't any more hard to learn than some other AI calculation. I composed this book to acquaint you with the rudiments of neural systems. You will coexist fine with undergrad level math and programming ability. 
Every one of the materials in this book can be downloaded and introduced for nothing. We will utilize the Python programming language, alongside the numerical figuring library Numpy. I will likewise show you in the later sections how to fabricate a profound system utilizing Theano and TensorFlow, which are libraries manufactured explicitly for profound learning and can quicken calculation by exploiting the GPU. 
Not at all like other AI calculations, profound learning is especially amazing on the grounds that it consequently learns highlights. That implies you don't have to invest your energy attempting to concoct and test "portions" or "collaboration impacts" - something just analysts love to do. Rather, we will give the neural system a chance to become familiar with these things for us. Each layer of the neural system learns an unexpected reflection in comparison to the past layers. For instance, in picture arrangement, the primary layer may learn various strokes, and in the following layer set up the strokes together to learn shapes, and in the following layer set up the shapes together to frame facial highlights, and in the following layer have an elevated level portrayal of appearances. 
Over this, profound learning is known for winning a considerable amount Kaggle challenges. These are AI challenges that are available to anybody on the planet who are permitted to utilize any AI method they need. Profound learning is that incredible. 
Do you need a delicate prologue to this "dull craftsmanship", with useful code models that you can attempt immediately and apply to your own information? At that point this book is for you. 
Who is this book NOT for? 
Profound Learning and Neural Networks are normally educated at the upper-year undergrad level. That should give you some thought of the sort of learning you have to comprehend this sort of material.



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