Neural Networks and Deep Learning by Pat Nakamoto

Language: English

Category: Uncategorized


Posted on 2018-07-15, by manhneovn.

Description

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English | 24 Jun. 2018 | ASIN: B07F12Q94H | 217 Pages | PDF(conv) | 2.43 MB
Ready to crank up a deep neural network to get your self-driving car pick up the kids from school? Want to add 'Neural Networksâ„¢ and ËœDeep Learningâ„¢ to your LinkedIn profile?

Well, hold on there...

Before you embark on your epic journey into the world of deep learning, there is basic theory to march through first! Check out this exceptional bundle of 3 books¦

Whatâ„¢s Inside?

Book 1:
Neural Networks & Deep Learning: Deep Learning explained to your granny A visual introduction for beginners who want to make their own Deep Learning Neural Network¦

What you will gain from this book:

* A deep understanding of how Deep Learning works
* A basics comprehension on how to build a Deep Neural Network from scratch

Who this book is for:

* Beginners who want to approach the topic, but are too afraid of complex math to start!

* Two main Types of Machine Learning Algorithms
* A practical example of Unsupervised Learning
* What are Neural Networks?
* McCulloch-Pitts's Neuron
* Types of activation function
* Types of network architectures
* Learning processes
* Advantages and disadvantages
* Let us give a memory to our Neural Network
* The example of book writing Software
* Deep learning: the ability of learning to learn
* How does Deep Learning work?
* Main architectures and algorithms
* Main types of DNN
* Available Frameworks and libraries
* Convolutional Neural Networks
* Tunnel Vision
* Convolution
* The right Architecture for a Neural Network
* Test your Neural Network
* A general overview of Deep Learning
* What are the limits of Deep Learning?
* Deep Learning: the basics
* Layers, Learning paradigms, Training, Validation
* Main architectures and algorithms
* Models for Deep Learning
* Probabilistic graphic models
* Restricted Boltzmann Machines
* Deep Belief Networks

Book2:
Deep Learning: Deep Learning explained to your granny A guide for Beginners¦

Whatâ„¢s Inside?

* A general overview of Deep Learning* What are the limits of Deep Learning?
* Deep Learning: the basics
* Layers, Learning paradigms, Training, Validation
* Main architectures and algorithms
* Convolutional Neural Networks
* Models for Deep Learning
* Probabilistic graphic models
* Restricted Boltzmann Machines
* Deep Belief Networks
* Available Frameworks and libraries
* TensorFlow

Book 3:
Big Data: The revolution that is transforming our work, market and world¦

"Within 2 days we produce the same amount of data generated by at the beginning of the civilization until 2003", said Eric Schmidt in 2010. According to IBM, by 2020 the world will have generated a mass of data on the order of 40 zettabyte (1021Byte). Just think, for example, of digital content such as photos, videos, blogs, posts, and everything that revolves around social networks; only Facebook marks 30 billion pieces of content each month shared by its users. The explosion of social networks, combined with the emergence of smartphones, justifies the fact that one of the recurring terms of recent years in the field of innovation, marketing and IT is "Big Data".
The term Big Data indicates data produced in massive quantities, with remarkable rapidity and in the most diverse formats, which require technologies and resources that go far beyond conventional data management and storage systems. In order to obtain from the use of this data the maximum results in the shortest possible time or even in real time, specific tools with high computing capabilities are necessary.
But what does the Big Data phenomenon mean? Is the proliferation of data simply the sign of an increasingly invasive world? Or is there something more to it?
Pat Nakamoto will guide you through the discovery of the world of Big data, which, according to experts, in the near future could become the new gold or oil, in what is a real Data Driven economy.
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