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:
not_spamin an email filter
bad_guyin fraud detection
happy_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.
2. Neural Networks and Computing book ( pdf )
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.
- 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
File size: 4.85 MB
File format: PDF