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Course Outline
- Machine Learning Limitations
- Machine Learning, Non-linear mappings
- Neural Networks
- Non-Linear Optimization, Stochastic/MiniBatch Gradient Decent
- Back Propagation
- Deep Sparse Coding
- Sparse Autoencoders (SAE)
- Convolutional Neural Networks (CNNs)
- Successes: Descriptor Matching
- Stereo-based Obstacle
- Avoidance for Robotics
- Pooling and invariance
- Visualization/Deconvolutional Networks
- Recurrent Neural Networks (RNNs) and their optimizaiton
- Applications to NLP
- RNNs continued,
- Hessian-Free Optimization
- Language analysis: word/sentence vectors, parsing, sentiment analysis, etc.
- Probabilistic Graphical Models
- Hopfield Nets, Boltzmann machines
- Deep Belief Nets, Stacked RBMs
- Applications to NLP, Pose and Activity Recognition in Videos
- Recent Advances
- Large-Scale Learning
- Neural Turing Machines
Requirements
Good understanding of Machine Learning. At least theoretical knowledge of Deep Learning.
Testimonials
Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.
Paul Kassis
Advanced Deep Learning Course
The exercises are sufficiently practical and do not need a high knowledge in Python to be done.
Alexandre GIRARD
Advanced Deep Learning Course
The global overview of deep learning
Bruno Charbonnier
Advanced Deep Learning Course
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