Part 1 – Deep Learning and DNN Concepts
Introduction AI, 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)
Basic Concepts of a Neural Network (Application: multi-layer 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, back-propagation, 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 Multi-feature 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
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, Sci-kit
DL high level frameworks: PyTorch, Keras, Lasagne
Low level DL frameworks: Theano, Torch, Caffe, Tensorflow
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: super-resolution, pixel-to-pixel segmentation. Presentation of
Main strategies for increasing feature maps for image generation.
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.
Generational models: Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN).
Presentation of the generational models, link with the CNNs
Auto-encoder: reduction of dimensionality and limited generation
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
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.
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. On-policy && off-policy. Actor critic architecture. A3C.
Applications: control of a single video game or a digital system.
Part 2 – Theano for Deep Learning
Theano Basics
Introduction
Installation and Configuration
Theano Functions
inputs, outputs, updates, givens
Training and Optimization of a neural network using Theano
Neural Network Modeling
Logistic Regression
Hidden Layers
Training a network
Computing and Classification
Optimization
Log Loss
Testing the model
Part 3 – DNN using Tensorflow
TensorFlow Basics
Creation, Initializing, Saving, and Restoring TensorFlow variables
Feeding, Reading and Preloading TensorFlow Data
How to use TensorFlow infrastructure to train models at scale
Visualizing and Evaluating models with TensorBoard
TensorFlow Mechanics
Prepare the Data
Download
Inputs and Placeholders
Build the GraphS
Inference
Loss
Training
Train the Model
The Graph
The Session
Train Loop
Evaluate the Model
Build the Eval Graph
Eval Output
The Perceptron
Activation functions
The perceptron learning algorithm
Binary classification with the perceptron
Document classification with the perceptron
Limitations of the perceptron
From the Perceptron to Support Vector Machines
Kernels and the kernel trick
Maximum margin classification and support vectors
Artificial Neural Networks
Nonlinear decision boundaries
Feedforward and feedback artificial neural networks
Multilayer perceptrons
Minimizing the cost function
Forward propagation
Back propagation
Improving the way neural networks learn
Convolutional Neural Networks
Goals
Model Architecture
Principles
Code Organization
Launching and Training the Model
Evaluating a Model
Basic Introductions to be given to the below modules(Brief Introduction to be provided based on time availability):
Tensorflow - Advanced Usage
Threading and Queues
Distributed TensorFlow
Writing Documentation and Sharing your Model
Customizing Data Readers
Manipulating TensorFlow Model Files
TensorFlow Serving
Introduction
Basic Serving Tutorial
Advanced Serving Tutorial
Serving Inception Model Tutorial
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