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上课地点:【上海】:同济大学(沪西)/新城金郡商务楼(11号线白银路站) 【深圳分部】:电影大厦(地铁一号线大剧院站)/深圳大学成教院 【北京分部】:北京中山学院/福鑫大楼 【南京分部】:金港大厦(和燕路) 【武汉分部】:佳源大厦(高新二路) 【成都分部】:领馆区1号(中和大道) 【广州分部】:广粮大厦 【西安分部】:协同大厦 【沈阳分部】:沈阳理工大学/六宅臻品 【郑州分部】:郑州大学/锦华大厦 【石家庄分部】:河北科技大学/瑞景大厦
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课程大纲
 
  • The course is divided into three separate days, the third being optional.
  • Day 1 Machine Learning & Deep Learning: theoretical concepts
    1. Introduction IA, Machine Learning & Deep Learning
  • History, basic concepts and usual applications of artificial intelligence far
  • Of the fantasies carried by this domain
  • Collective Intelligence: aggregating knowledge shared by many virtual agents
  • Genetic algorithms: to evolve a population of virtual agents by selection
  • Usual Learning Machine: definition.
  • Types of tasks: supervised learning, unsupervised learning, reinforcement learning
  • Types of actions: classification, regression, clustering, density estimation, reduction of
  • dimensionality
  • Examples of Machine Learning algorithms: Linear regression, Naive Bayes, Random Tree
  • Machine learning VS Deep Learning: problems on which Machine Learning remains
  • Today the state of the art (Random Forests & XGBoosts)
  • 2. Basic Concepts of a Neural Network (Application: multilayer perceptron)
  • Reminder of mathematical bases.
  • Definition of a network of neurons: classical architecture, activation and
  • Weighting of previous activations, depth of a network
  • Definition of the learning of a network of neurons: functions of cost, backpropagation,
  • Stochastic gradient descent, maximum likelihood.
  • Modeling of a neural network: modeling input and output data according to
  • The type of problem (regression, classification ...). Curse of dimensionality. Distinction between
  • Multifeature data and signal. Choice of a cost function according to the data.
  • Approximation of a function by a network of neurons: presentation and examples
  • Approximation of a distribution by a network of neurons: presentation and examples
  • Data Augmentation: how to balance a dataset
  • Generalization of the results of a network of neurons.
  • Initialization and regularization of a neural network: L1 / L2 regularization, Batch
  • Normalization ...
  • Optimization and convergence algorithms.
  • 3. Standard ML / DL Tools
  • A simple presentation with advantages, disadvantages, position in the ecosystem and use is planned.
  • Data management tools: Apache Spark, Apache Hadoop
  • Tools Machine Learning: Numpy, Scipy, Scikit
  • DL high level frameworks: PyTorch, Keras, Lasagne
  • Low level DL frameworks: Theano, Torch, Caffe, Tensorflow
  • Day 2 Convolutional and Recurrent Networks
    4. Convolutional Neural Networks (CNN).
  • Presentation of the CNNs: fundamental principles and applications
  • Basic operation of a CNN: convolutional layer, use of a kernel,
  • Padding & stride, feature map generation, pooling layers. Extensions 1D, 2D and
  • 3D.
  • Presentation of the different CNN architectures that brought the state of the art in classification
  • Images: LeNet, VGG Networks, Network in Network, Inception, Resnet. Presentation of
  • Innovations brought about by each architecture and their more global applications (Convolution
  • 1x1 or residual connections)
  • Use of an attention model.
  • Application to a common classification case (text or image)
  • CNNs for generation: superresolution, pixeltopixel segmentation. Presentation of
  • Main strategies for increasing feature maps for image generation.
  • 5. Recurrent Neural Networks (RNN).
  • Presentation of RNNs: fundamental principles and applications.
  • Basic operation of the RNN: hidden activation, back propagation through time,
  • Unfolded version.
  • Evolutions towards the Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory).
  • Presentation of the different states and the evolutions brought by these architectures
  • Convergence and vanising gradient problems
  • Classical architectures: Prediction of a temporal series, classification ...
  • RNN Encoder Decoder type architecture. Use of an attention model.
  • NLP applications: word / character encoding, translation.
  • Video Applications: prediction of the next generated image of a video sequence.
  • Day 3 Generational Models and Reinforcement Learning
    6. Generational models: Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN).
  • Presentation of the generational models, link with the CNNs seen in day 2
  • Autoencoder: reduction of dimensionality and limited generation
  • Variational Autoencoder: generational model and approximation of the distribution of a
  • given. Definition and use of latent space. Reparameterization trick. Applications and
  • Limits observed
  • Generative Adversarial Networks: Fundamentals. Dual Network Architecture
  • (Generator and discriminator) with alternate learning, cost functions available.
  • Convergence of a GAN and difficulties encountered.
  • Improved convergence: Wasserstein GAN, Began. Earth Moving Distance.
  • Applications for the generation of images or photographs, text generation, super
    resolution.
  • 7. Deep Reinforcement Learning.
  • Presentation of reinforcement learning: control of an agent in a defined environment
  • By a state and possible actions
  • Use of a neural network to approximate the state function
  • Deep Q Learning: experience replay, and application to the control of a video game.
  • Optimization of learning policy. Onpolicy && offpolicy. Actor critic
  • architecture. A3C.
  • Applications: control of a single video game or a digital system.
 
 
  备案号:备案号:沪ICP备08026168号-1 .(2014年7月11)...................
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