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
- 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.
1. Applied Deep Learning book ( pdf )
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
File size: 12.5 MB
File format: PDF
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