斯坦福、伯克利、杜克大学、哥大等 深度学习课程+书籍

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发表于 2021-7-11 06:06:09 | 显示全部楼层 |阅读模式
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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