【V8466】斯坦福、伯克利、杜克大学、哥大等 深度学习课程+书籍(精华)

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斯坦福、伯克利、杜克大学、哥大等 深度学习课程+书籍
深度学习是机器学习研究中的一个新的领域,其动机在于建立、模拟人脑进行分析学习的神经网络,它模仿人脑的机制来解释数据,例如图像,声音和文本。
【课程内容】
Audio Signal Processing for Music Applications

Introduction
Discrete Fourier transform
Fourier theorems
Short-time Fourier transform
Sinusoidal model
Harmonic model
Sinusoidal plus residual model
Sound transformations
Sound and music description
Concluding topics

Computer Vision 计算机视觉

Overview
Fundamentals of image formation
Rigid body motion
Orthogonal transformations
Orthogonal transformations - Orthogonal Matrices
Orthogonal matrices - Rotations and reflections
Parametrizing Rotations in 3D
Euclidean, Affine and Projective Transformations
Dynamic Perspective
Binocular Stereo
Radiometry
Image processing
Orientation histograms
Handwritten digit recognition - Introduction
Support Vector Machines
Transformation Invariance and Histograms
Digit recognition using SVMs
Random forests
Detection of 3D objects
Concluding Remarks

Image and video processing

What is image and video processing
Course logistics
Images are everywhere
Human visual system
Image formation - Sampling  Quantization
Simple image operations
The why and how of compression
Huffman coding
JPEGs 8x8 blocks
The Discrete Cosine Transform (DCT)
Quantization
JPEG_LS and MPEG
Bonus Run-length compression
Introduction to image enhancement
Demo - Enhancement Histogram modification
Histogram equalization
Histogram matching
Introduction to local neighborhood operations
Mathematical properties of averaging
Non-Local means
IPOL Demo - Non-Local means
Median filter
Demo - Median filter
Derivatives Laplacian  Unsharp masking
Demo - Unsharp masking
Gradients of scalar and vector images
Concluding remarks
What is image restoration
Noise types
Demo - Types of noise
Noise and histograms
Estimating noise
Degradation Function
Wiener filtering
Demo - Wiener and Box filters
Concluding remarks
Introduction to Segmentation
On Edges and Regions
Hough Transform with Matlab Demo
Line Segment Detector with Demo
Otsus Segmentation with Demo
Congratulations
Interactive Image Segmentation
Graph Cuts and Ms Office
Mumford-Shah
Active Contours - Introduction with ipol.im and Photoshop Demos
Behind the Scenes of Adobes Roto Brush
Introduction to PDEs in Image and Video Processing
Planar Differential Geometry
Surface Differential Geometry
Curve Evolution
Level Sets and Curve Evolution
Calculus of Variations
Anisotropic Diffusion
Active Contours
Bonus Cool Contrast Enhancement via PDEs
Introduction to Image Inpainting
Inpainting in Nature
PDEs and Inpainting
Inpainting via Calculus of Variations
Smart Cut and Paste
Demo - Photoshop Inpainting Healing Brush
Video Inpainting and Conclusions
Introduction to Sparse Modeling
Sparse Modeling - Implementation
Dictionary Learning
Sparse Modeling Image Processing Examples
A Note on Compressed Sensing
GMM and Structured Sparsity
Bonus Sparse Modeling and Classification - Activity Recognition
Introduction to Medical Imaging
Image Processing and HIV
Brain Imaging Diffusion Imaging Deep Brain Stimulation

Natural Language Processing Collins

Introduction to Natural Language Processing
The Language Modeling Problem
Parameter Estimation in Language Models
Summary
Tagging Problems and Hidden Markov Models
Parsing and Context-Free Grammars
Probabilistic Context-Free Grammars
Weaknesses of PCFGs
Lexicalized PCFGs
Introduction to Machine Translation
The IBM Translation Models
Phrase-based Translation Models
Decoding of Phrase-based Translation Models
Log-linear Models
Log-linear Models for Tagging
Log-Linear Models for History-based Parsing
Unsupervised Learning- Brown Clustering
Global Linear Models
GLMs for Tagging
GLMs for Dependency Parsing

Neural Networks for Machine Learning

hinton-ml(67课)
neuralnets(78课)

Probabilistic Graphical Models

Introduction and Overview
Bayesian Network Fundamentals
Template Models
ML-class Octave Tutorial
Structured CPDs
Markov Network Fundamentals
Representation Wrapup- Knowledge Engineering
Inference-Variable Elimination
Inference-Belief Propagation
Inference-MAP Estimation
Inference-Sampling Methods
Inference-Temporal Models and Wrap-up
Decision Theory
ML-class Revision
Learning-Overview
Learning-Parameter Estimation in BNs
Learning-Parameter Estimation in MNs
Structure Learning
Learning With Incomplete Data
Learning-Wrapup
Summary

《深度学习在互联网上的应用》
神经网络、深度学习方向书籍资料

A Note on BPTT for LSTM LM.pdf
cnn-lstm-ctc.pdf
CNN与反向传播.pdf
ctc.pdf
Deep learning(1).pdf
Deep Learning-Bengio .pdf
deep learning.pdf
deep-learning-nature2015.pdf
deeplearning.pdf
deeplearningbook-chinese-master.zip
DeepLearningBook.pdf
DeepLearning_MethodsandApplications-MR-Chinese.pdf
deep_rl_tutorial.pdf
Hinton.SOM.pdf
Introduction to Deep Learning.pdf
Neural Network and Deep Learning.pdf
Supervised Sequence Labelling with Recurrent Neural Networks.pdf
tr.pdf
Unsupervised Learning of Edges_Yin Li_2016.pdf
Week1d Introduction to CNNs (AlexNet).pdf
《神经网络与深度学习》邱锡鹏
《神经网络与深度学习综述DeepLearning15May2014.pdf

人工智能深度学习deeplearning_for_AI_course(2015_Spring)_927202100.pdf
刘昕 - 深度学习基础与实战_2017新版.pdf
可视化理解卷积网络Visualizing and Understanding Convolutional Networks.pdf
吴恩达深度学习基础教程.pdf
基于CNN的图片颜色处理.pdf
基于卷积神经网络的深度学习算法与应用研究.pdf
大数据,机器(深度)学习精品名师学习课程.pdf
深度学习.rar
深度学习word2vec学习笔记.pdf
深度学习基础及数学原理.pdf
深度学习基础教程.pdf
深度学习的基本理论与方法.pptx

电子书_深度学习方法及应用.pdf
神经网络和深度学习.pdf
神经网络与机器学习(原书第3版).pdf
神经网络与深度学习讲义20151211.pdf
神经网络原理.pdf

 

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