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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.
28 Hours
Testimonials (4)
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course - Advanced Deep Learning
Doing exercises on real examples using Eras. Italy totally understood our expectations about this training.
Paul Kassis
Course - Advanced Deep Learning
The exercises are sufficiently practical and do not need high knowledge in Python to be done.
Alexandre GIRARD
Course - Advanced Deep Learning
The global overview of deep learning.
