斯坦福、伯克利、杜克大学、哥大等 深度学习课程+书籍
602395;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
IntroductionDiscrete Fourier transformFourier theoremsShort-time Fourier transformSinusoidal modelHarmonic modelSinusoidal plus residual modelSound transformationsSound and music descriptionConcluding topics
Computer Vision 计算机视觉
OverviewFundamentals of image formationRigid body motionOrthogonal transformationsOrthogonal transformations - Orthogonal MatricesOrthogonal matrices - Rotations and reflectionsParametrizing Rotations in 3DEuclidean, Affine and Projective TransformationsDynamic PerspectiveBinocular StereoRadiometryImage processingOrientation histogramsHandwritten digit recognition - IntroductionSupport Vector MachinesTransformation Invariance and HistogramsDigit recognition using SVMsRandom forestsDetection of 3D objectsConcluding Remarks
Image and video processing
What is image and video processingCourse logisticsImages are everywhereHuman visual systemImage formation - Sampling QuantizationSimple image operationsThe why and how of compressionHuffman codingJPEGs 8x8 blocksThe Discrete Cosine Transform (DCT)QuantizationJPEG_LS and MPEGBonus Run-length compressionIntroduction to image enhancementDemo - Enhancement Histogram modificationHistogram equalizationHistogram matchingIntroduction to local neighborhood operationsMathematical properties of averagingNon-Local meansIPOL Demo - Non-Local meansMedian filterDemo - Median filterDerivatives Laplacian Unsharp maskingDemo - Unsharp maskingGradients of scalar and vector imagesConcluding remarksWhat is image restorationNoise typesDemo - Types of noiseNoise and histogramsEstimating noiseDegradation FunctionWiener filteringDemo - Wiener and Box filtersConcluding remarksIntroduction to SegmentationOn Edges and RegionsHough Transform with Matlab DemoLine Segment Detector with DemoOtsus Segmentation with DemoCongratulationsInteractive Image SegmentationGraph Cuts and Ms OfficeMumford-ShahActive Contours - Introduction with ipol.im and Photoshop DemosBehind the Scenes of Adobes Roto BrushIntroduction to PDEs in Image and Video ProcessingPlanar Differential GeometrySurface Differential GeometryCurve EvolutionLevel Sets and Curve EvolutionCalculus of VariationsAnisotropic DiffusionActive ContoursBonus Cool Contrast Enhancement via PDEsIntroduction to Image InpaintingInpainting in NaturePDEs and InpaintingInpainting via Calculus of VariationsSmart Cut and PasteDemo - Photoshop Inpainting Healing BrushVideo Inpainting and ConclusionsIntroduction to Sparse ModelingSparse Modeling - ImplementationDictionary LearningSparse Modeling Image Processing ExamplesA Note on Compressed SensingGMM and Structured SparsityBonus Sparse Modeling and Classification - Activity RecognitionIntroduction to Medical ImagingImage Processing and HIVBrain Imaging Diffusion Imaging Deep Brain Stimulation
Natural Language Processing Collins
Introduction to Natural Language ProcessingThe Language Modeling ProblemParameter Estimation in Language ModelsSummaryTagging Problems and Hidden Markov ModelsParsing and Context-Free GrammarsProbabilistic Context-Free GrammarsWeaknesses of PCFGsLexicalized PCFGsIntroduction to Machine TranslationThe IBM Translation ModelsPhrase-based Translation ModelsDecoding of Phrase-based Translation ModelsLog-linear ModelsLog-linear Models for TaggingLog-Linear Models for History-based ParsingUnsupervised Learning- Brown ClusteringGlobal Linear ModelsGLMs for TaggingGLMs for Dependency Parsing
Neural Networks for Machine Learning
hinton-ml(67课)neuralnets（78课）
Probabilistic Graphical Models
Introduction and OverviewBayesian Network FundamentalsTemplate ModelsML-class Octave TutorialStructured CPDsMarkov Network FundamentalsRepresentation Wrapup- Knowledge EngineeringInference-Variable EliminationInference-Belief PropagationInference-MAP EstimationInference-Sampling MethodsInference-Temporal Models and Wrap-upDecision TheoryML-class RevisionLearning-OverviewLearning-Parameter Estimation in BNsLearning-Parameter Estimation in MNsStructure LearningLearning With Incomplete DataLearning-WrapupSummary
《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.pdfcnn-lstm-ctc.pdfCNN与反向传播.pdfctc.pdfDeep learning(1).pdfDeep Learning-Bengio .pdfdeep learning.pdfdeep-learning-nature2015.pdfdeeplearning.pdfdeeplearningbook-chinese-master.zipDeepLearningBook.pdfDeepLearning_MethodsandApplications-MR-Chinese.pdfdeep_rl_tutorial.pdfHinton.SOM.pdfIntroduction to Deep Learning.pdfNeural Network and Deep Learning.pdfSupervised Sequence Labelling with Recurrent Neural Networks.pdftr.pdfUnsupervised Learning of Edges_Yin Li_2016.pdfWeek1d 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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