Python Learning (ii)--Introduction to deep learning

　　　　　　　　　　　　　　　　　　Lesson two: Getting Started with deep learning

Lecturer: David (data analysis engineer)

This course mainly introduces the basic principles of many neural networks, very very basic understanding.

0, Mind map preview:

One, deep neural network

1. How neurons work

　　

--This is a biological neuron, which is then abstracted from it and made into a m-p neuron pattern.

2. Introduction to Neural networks

--1943 M-p Neuron model

　　　　

--1956 Sensing Machine

--1986 Distributed Representation

by Hinton (the father of neural networks?) ) Proposed

-- 9 combinations of 6 neurons .

Red Dog

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Neuron color Object	Red	Yellow	Blue
Car	Red car	Yellow car	Blue car
Horse	Red horse	Huang Ma	Horse
Dog	Yellow Dog	Blue Dog

In the beginning, 9 neurons were needed to represent these combinations, and then after the distributed representation, 6 neurons could be used , and by their 22 combinations, 9 combinations were realized. This method.

--1986 Inverse propagation algorithm

--1994 long and short memory network

--2006 Deep Neural Network

--2007 convolutional Neural network

　　

3. Why do you learn so much in depth now?

--"Big" data

At present, the technology development is better, the network has rich data.

Deep learning: It takes a lot of data to train his abilities.

--"Deep" model

The computing power of the current computer is strong.

4. Neural network classification

--Feedforward Neural network

　　　　

--Deep neural network (full-link)

　　

--Optimizing deep neural networks

TensorFlow (more popular),torch ,Theano ,Caffe ,mxnet , pytorch

-- test:http://playground.tensorflow.org A tool test for optimizing deep neural networks

--convolutional neural networks

--dealing with the problem of image recognition

　　　

--Cyclic neural network (RNN)

　　　　

--long and short Memory network (LSTM)

　　

--Gate Loop Network (GRU)

　　

--Production discriminant network

　　

Second, the application of deep learning

1. Image recognition

　　

2. Language recognition

3. Machine translation

4. Image generation

Third, how to learn deep learning

1. Mathematics

--Linear algebra

--Calculus

--Convex Optimization calculation method

--Probability theory, etc.

2. Machine learning

3. Programming

--Algorithms and data structures

--python

4. Deep learning

--Related Materials recommended:

Deep learning,Lan Goodfellow, Yoshua ,Bengio, etc.

--Paper website recommendation: Domain Name:arXiv

--Recommendation of relevant public courses

CS231N & Hinton

　　

　　

Python Learning (ii)--Introduction to deep learning