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Course Outline
The course is separated into three distinct days, the third being optional.
Day 1 - Machine Learning & Deep Learning: theoretical concepts
1. AI Introduction, Machine Learning & Deep Learning
- History, fundamental concepts and usual applications of artificial intelligence far from the fantasies carried by this field
- Collective intelligence: aggregate knowledge shared by numerous virtual agents
- Genetic algorithms: evolving a population of virtual agents by selection
- Machine Learning usual: definition.
- Types of tasks: supervised learning, unsupervised learning, reinforcement learning
- Types of actions: classification, regression, clustering, density estimation, dimensionality reduction
- Examples of algorithms Machine Learning: Linear regression, Naive Bayes, Random Tree
- Machine learning VS Deep Learning: problems on which Machine Learning remains the state of the art today (Random Forests & XGBoosts)
2. Fundamental concepts of a neural network (Application: multi-layer perceptron)
- Reminder of mathematical basics.
- Definition of a neural network: classic architecture, activation and weighting functions of previous activations, depth of a network
- Definition of learning a neural network: cost functions, back-propagation, stochastic gradient descent, maximum likelihood.
- Modeling of a neural network: modeling of input and output data according to the type of problem (regression, classification, etc.). Curse of dimensionality. Distinction between multi-feature data and signal. Choice of a cost function according to the data.
- Approximate a function using a neural network: presentation and examples
- Approximating a distribution using a neural network: presentation and examples
- Data Augmentation: how to balance a dataset
- Generalization of the results of a neural network.
- Initializations and regularizations of a neural network: L1/L2 regularization, Batch Normalization...
- Optimizations and convergence algorithms.
3. Common ML/DL tools
A simple presentation with advantages, disadvantages, position in the ecosystem and use is planned.
- Data management tools: Apache Spark, Apache Hadoop
- Common tools Machine Learning: Numpy, Scipy, Sci-kit
- High-level DL 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 CNNs: fundamental principles and applications
- Fundamental operation of a CNN: convolutional layer, use of a kernel, padding & stride, generation of feature maps, 'pooling' type layers. 1D, 2D and 3D extensions.
- Presentation of the different CNN architectures that have brought the state of the art to image classification: LeNet, VGG Networks, Network in Network, Inception, Resnet. Presentation of the innovations brought by each architecture and their more global applications (1x1 Convolution or residual connections)
- Use of an attention model.
- Application to a usual classification scenario (text or image)
- CNNs for generation: super-resolution, pixel-to-pixel segmentation. Presentation of the main strategies for augmenting feature maps for generating an image.
5. Recurrent Neural Networks (RNN).
- Presentation of RNNs: fundamental principles and applications.
- Fundamental operation of the RNN: hidden activation, back propagation through time, unfolded version.
- Developments towards GRU (Gated Recurrent Units) and LSTM (Long Short Term Memory). Presentation of the different states and the developments brought about by these architectures
- Convergence and vanishing gradient problems
- Types of classic architectures: Prediction of a time series, classification...
- RNN Encoder Decoder type architecture. Using 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 generational models, link with CNNs seen on day 2
- Auto-encode: dimensionality reduction and limited generation
- Variational Auto-encoder: generational model and approximation of the distribution of data. Definition and use of latent space. Reparameterization trick. Applications and observed limits
- Generative Adversarial Networks: fundamental principles. Two-network architecture (generator and discriminator) with alternating learning, cost functions available.
- Convergence of a GAN and difficulties encountered.
- Improved convergence: Wasserstein GAN, BeGAN. Earth Moving Distance.
- Applications for generating images or photographs, generating text, super
resolution.
7.DeepReinforcement Learning.
- Presentation of reinforcement learning: control of an agent in an environment defined by a state and possible actions
- Using a neural network to approximate the state function
- Deep Q Learning: experience replay, and application to the control of a video game.
- Optimizations of the learning policy. On-policy && off-policy. Actor critical architecture. A3C.
- Applications: control of a simple video game or a digital system.
Requirements
Engineer level
21 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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