NeuralNetworksandDeepLearning_DeepLearni.epub - (EPUB全文下载)

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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
 
 
 
 
 
 
 
 
 
 
By
Pat Nakamoto
 
 
 
Contents
Introduction
Neural Networks and Deep Learning
Chapter 1.  A brief introduction to Machine Learning
Notes to this Chapter
What is Machine Learning?
Two main Types of Machine Learning Algorithms
A practical example of Unsupervised Learning
Key points of this Chapter
Chapter 2.  Neural Networks
Notes to this Chapter
What are Neural Networks?
McCulloch-Pitts's Neuron
Types of activation function
Types of network architectures
Learning processes
Advantages and disadvantages
Key points of this Chapter
Chapter 3. Deep Learning
Notes to this Chapter
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
Key points of this Chapter
Chapter 4. Convolutional Neural Networks
Notes to this Chapter
Deep Learning and Convolutional Neural Networks (CNNs)
Tunnel Vision
Convolution
The right Architecture for a Neural Network
Test your Neural Network
Key points of this Chapter
CONCLUSIONS
Introduction
 
“A sinister threat is brewing deep inside the technology laboratories of Silicon Valley. Artificial Intelligence, disguised as helpful digital assistants and self-driving vehicles, is gaining a foothold -- and it could one day spell the end for mankind”.
ELLIE ZOLFAGHARIFARD, Mail Online
 
You certainly have noticed that Machine Learning and Artificial Intelligence are seemingly the biggest technological hype waves of the moment. World giant players, such as Apple, Amazon, IBM, Google and Facebook, heavily invest in machine learning research and applications for good reasons. However, what is this hype all about?
Machine Learning applications are already very numerous today, and some have entered our daily lives without us actually realizing it. For example, think of search engines: when you insert one or more keyword, a search engine returns a list of result, called SERP (Search Engine Results Pages) that are the effect of Machine Learning algorithms using unsupervised learning (providing as output information deemed relevant for the research carried out, based on the analysis of schemas, models, and structures in data).
Another common example is email spam filters based on Mach ............

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