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Course Outline
Part 1 – Deep Learning and DNN Concepts
Introduction AI, Machine Learning & Deep Learning
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History, basic concepts and usual applications of artificial intelligence far Of the fantasies carried by this domain
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Collective Intelligence: aggregating knowledge shared by many virtual agents
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Genetic algorithms: to evolve a population of virtual agents by selection
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Usual Learning Machine: definition.
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Types of tasks: supervised learning, unsupervised learning, reinforcement learning
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Types of actions: classification, regression, clustering, density estimation, reduction of dimensionality
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Examples of Machine Learning algorithms: Linear regression, Naive Bayes, Random Tree
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Machine learning VS Deep Learning: problems on which Machine Learning remains Today the state of the art (Random Forests & XGBoosts)
Basic Concepts of a Neural Network (Application: multi-layer perceptron)
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Reminder of mathematical bases.
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Definition of a network of neurons: classical architecture, activation and
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Weighting of previous activations, depth of a network
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Definition of the learning of a network of neurons: functions of cost, back-propagation, Stochastic gradient descent, maximum likelihood.
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Modeling of a neural network: modeling input and output data according to The type of problem (regression, classification ...). Curse of dimensionality.
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Distinction between Multi-feature data and signal. Choice of a cost function according to the data.
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Approximation of a function by a network of neurons: presentation and examples
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Approximation of a distribution by a network of neurons: presentation and examples
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Data Augmentation: how to balance a dataset
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Generalization of the results of a network of neurons.
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Initialization and regularization of a neural network: L1 / L2 regularization, Batch Normalization
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Optimization and convergence algorithms
Standard ML / DL Tools
A simple presentation with advantages, disadvantages, position in the ecosystem and use is planned.
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Data management tools: Apache Spark, Apache Hadoop Tools
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Machine Learning: Numpy, Scipy, Sci-kit
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DL high level frameworks: PyTorch, Keras, Lasagne
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Low level DL frameworks: Theano, Torch, Caffe, Tensorflow
Convolutional Neural Networks (CNN).
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Presentation of the CNNs: fundamental principles and applications
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Basic operation of a CNN: convolutional layer, use of a kernel,
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Padding & stride, feature map generation, pooling layers. Extensions 1D, 2D and 3D.
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Presentation of the different CNN architectures that brought the state of the art in classification
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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)
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Use of an attention model.
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Application to a common classification case (text or image)
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CNNs for generation: super-resolution, pixel-to-pixel segmentation. Presentation of
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Main strategies for increasing feature maps for image generation.
Recurrent Neural Networks (RNN).
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Presentation of RNNs: fundamental principles and applications.
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Basic operation of the RNN: hidden activation, back propagation through time, Unfolded version.
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Evolutions towards the Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory).
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Presentation of the different states and the evolutions brought by these architectures
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Convergence and vanising gradient problems
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Classical architectures: Prediction of a temporal series, classification ...
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RNN Encoder Decoder type architecture. Use of an attention model.
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NLP applications: word / character encoding, translation.
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Video Applications: prediction of the next generated image of a video sequence.
Generational models: Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN).
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Presentation of the generational models, link with the CNNs
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Auto-encoder: reduction of dimensionality and limited generation
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Variational Auto-encoder: generational model and approximation of the distribution of a given. Definition and use of latent space. Reparameterization trick. Applications and Limits observed
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Generative Adversarial Networks: Fundamentals.
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Dual Network Architecture (Generator and discriminator) with alternate learning, cost functions available.
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Convergence of a GAN and difficulties encountered.
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Improved convergence: Wasserstein GAN, Began. Earth Moving Distance.
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Applications for the generation of images or photographs, text generation, super-resolution.
Deep Reinforcement Learning.
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Presentation of reinforcement learning: control of an agent in a defined environment
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By a state and possible actions
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Use of a neural network to approximate the state function
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Deep Q Learning: experience replay, and application to the control of a video game.
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Optimization of learning policy. On-policy && off-policy. Actor critic architecture. A3C.
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Applications: control of a single video game or a digital system.
Part 2 – Theano for Deep Learning
Theano Basics
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Introduction
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Installation and Configuration
TheanoFunctions
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inputs, outputs, updates, givens
Training and Optimization of a neural network using Theano
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Neural Network Modeling
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Logistic Regression
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Hidden Layers
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Training a network
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Computing and Classification
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Optimization
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Log Loss
Testing the model
Part 3 – DNN using Tensorflow
TensorFlow Basics
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Creation, Initializing, Saving, and Restoring TensorFlow variables
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Feeding, Reading and Preloading TensorFlow Data
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How to use TensorFlow infrastructure to train models at scale
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Visualizing and Evaluating models with TensorBoard
TensorFlow Mechanics
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Prepare the Data
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Download
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Inputs and Placeholders
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Build the GraphS
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Inference
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Loss
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Training
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Train the Model
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The Graph
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The Session
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Train Loop
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Evaluate the Model
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Build the Eval Graph
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Eval Output
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The Perceptron
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Activation functions
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The perceptron learning algorithm
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Binary classification with the perceptron
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Document classification with the perceptron
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Limitations of the perceptron
From the Perceptron to Support Vector Machines
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Kernels and the kernel trick
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Maximum margin classification and support vectors
Artificial Neural Networks
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Nonlinear decision boundaries
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Feedforward and feedback artificial neural networks
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Multilayer perceptrons
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Minimizing the cost function
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Forward propagation
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Back propagation
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Improving the way neural networks learn
Convolutional Neural Networks
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Goals
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Model Architecture
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Principles
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Code Organization
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Launching and Training the Model
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Evaluating a Model
Basic Introductions to be given to the below modules(Brief Introduction to be provided based on time availability):
Tensorflow - Advanced Usage
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Threading and Queues
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Distributed TensorFlow
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Writing Documentation and Sharing your Model
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Customizing Data Readers
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Manipulating TensorFlow Model Files
TensorFlow Serving
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Introduction
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Basic Serving Tutorial
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Advanced Serving Tutorial
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Serving Inception Model Tutorial
Requirements
Background in physics, mathematics and programming. Involvment in image processing activities.
The delegates should have a prior understanding of machine learning concepts, and should have worked upon Python programming and libraries.
35 Hours
