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Best Deep Learning and Neural networks E-books 2018 [PDF]

Deep Learning and Neural networks

A collection of the Best Deep Learning and Neural networks E-books UPDATED 2018

What is deep learning ?

Deep learning is a subset of AI and machine learning that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation and others.
Deep learning differs from traditional machine learning techniques in that they can automatically learn representations from data such as images, video or text, without introducing hand-coded rules or human domain knowledge. Their highly flexible architectures can learn directly from raw data and can increase their predictive accuracy when provided with more data.

Most known deep learning examples/applications

  1. Google DeepMind’s AlphaGo
  2. Self-driving car ( Robot car )
  3. Voice assistant technology (Virtual assistant )



What is a neural network

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.
Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. (Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.)

Neural networks applications

What kind of problems does deep learning and neural networks solve, and more importantly, can it solve yours? To know the answer, you need to ask questions:

What outcomes do I care about? Those outcomes are labels that could be applied to data:For example:

Do I have the data to accompany those labels? That is, can I find labeled data, or can I create a labeled dataset (with a service like AWS Mechanical Turk or Figure Eight or Mighty.ai) where spam has been labeled as spam, in order to teach an algorithm the correlation between labels and inputs?

So here i am going to list the best pdf books that it contains deep learning and neural networks How to etc  tutorials and courses for beginners and scientists.

Applied Deep Learning book ( pdf )







Description:

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function.

The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions.

What You Will Learn

Author: Umberto Michelucci
ISBN-10: 1484237897
Year: 2018
Pages: 410
Language: English
File size: 12.5 MB
File format: PDF

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Neural Networks and Computing book ( pdf )




Description:

This book covers neural networks with special emphasis on advanced learning methodologies and applications.It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been covered by many existing books in this area. Additionally, the book highlights the important feature selection problem, which baffles many neural networks practitioners because of the difficulties handling large datasets. It also contains several interesting IT, engineering and bioinformatics applications.

Contents:

Author: Tommy W. S. Chow
ISBN-10: 1860947581
Year: 2007
Pages: 324
Language: English
File size: 4.85 MB
File format: PDF

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Complex-Valued Neural Networks: Advances and Applications book ( pdf )




Description:

Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications

Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and superconducting waves. This fact is a critical advantage in practical applications in diverse fields of engineering, where signals are routinely analyzed and processed in time/space, frequency, and phase domains.

Complex-Valued Neural Networks: Advances and Applications covers cutting-edge topics and applications surrounding this timely subject. Demonstrating advanced theories with a wide range of applications, including communication systems, image processing systems, and brain-computer interfaces, this text offers comprehensive coverage of:

  • Conventional complex-valued neural networks
  • Quaternionic neural networks
  • Clifford-algebraic neural networks

Author: Akira Hirose
ISBN-10: 111834460X
Year: 2013
Pages: 304
Language: English
File size: 7.27 MB
File format: PDF

Neural Network Programming with Java book ( pdf )

Description:

This book gives you a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java.

You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using the concepts you’ve learned. Furthermore, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time.

All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience.

What You Will Learn
  • Get to grips with the basics of neural networks and what they are used for
  • Develop neural networks using hands-on examples
  • Explore and code the most widely-used learning algorithms to make your neural network learn from most types of data
  • Discover the power of neural network’s unsupervised learning process to extract the intrinsic knowledge hidden behind the data
  • Apply the code generated in practical examples, including weather forecasting and pattern recognition
  • Understand how to make the best choice of learning parameters to ensure you have a more effective application
  • Select and split data sets into training, test, and validation, and explore validation strategies
  • Discover how to improve and optimize your neural network

Author: Alan Souza, Fábio Soares
ISBN-10: 178588090X
Year: 2016
Pages: 191
Language: English
File size: 5.28 MB
File format: PDF

Exploring Neural Networks with C# book ( pdf )

Description:

The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations—making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical.

Exploring Neural Networks with C#</strong> presents the important properties of neural networks—while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand.

Table of Contents:

Author: Nabendu Chaki, Rituparna Chaki, Ryszard Tadeusiewicz
ISBN-10: 978-1482233391
Year: 2014
Pages: 298
Language: English
File size: 11.53 MB
File format: PDF

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