A collection of the Best Deep Learning and Neural networks E-books UPDATED 2018
What is deep learning ?
Most known deep learning examples/applications
- Google DeepMind’s AlphaGo
- Self-driving car ( Robot car )
- 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:
spamornot_spamin an email filtergood_guyorbad_guyin fraud detectionangry_customerorhappy_customerin customer relationship management.
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
- Implement advanced techniques in the right way in Python and TensorFlow
- Debug and optimize advanced methods (such as dropout and regularization)
- Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)
- Set up a machine learning project focused on deep learning on a complex dataset
Author: Umberto Michelucci
ISBN-10: 1484237897
Year: 2018
Pages: 410
Language: English
File size: 12.5 MB
File format: PDF
Download Applied Deep Learning book for free
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:
- Learning Performance and Enhancement
- Generalization and Performance Enhancement
- Basis Function Networks for Classification
- Self-Organizing Maps
- Classification and Feature Selection
- Engineering Applications
Author: Tommy W. S. Chow
ISBN-10: 1860947581
Year: 2007
Pages: 324
Language: English
File size: 4.85 MB
File format: PDF
Download Neural Networks and Computing book for free
Complex-Valued Neural Networks: Advances and Applications book ( pdf )
Description:
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:
- Introduction to Natural and Artificial Neural Networks.
- Neural Net Structure
- Teaching Networks.
- Functioning of Simplest Networks.
- Teaching Simple Linear One-Layer Neural Networks
- Nonlinear Networks
- Back propagation
- Forms of Neural Network Learning
- Self-Learning Neural Networks
- Self-Organizing Neural Networks
- Recurrent Networks
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