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 - SamplingQuantization
[*]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 LaplacianUnsharp 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
本资源来源于 网络 付费网站付费收集而来, 随时收集更新资源本站专注搜集和分享各种付费网站资源,感谢您的信任
资源下载地址:
**** Hidden Message *****
本站所有资源都来源于网络收集,网友提供或者交换而来!
如果侵犯了您的权益,请及时联系客服,我们即刻删除!
页:
[1]