28 hours (usually 4 days including breaks)
Good understanding of Machine Learning. At least theoretical knowledge of Deep Learning.
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.
- 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, Restricted Boltzmann Machines
- Hopfield Networks, (Restricted) Bolzmann Machines
- Deep Belief Nets, Stacked RBMs
- Applications to NLP , Pose and Activity Recognition in Videos
- Recent Advances
- Large-Scale Learning
- Neural Turing Machines
The global overview of deep learning
The exercises are sufficiently practical and do not need a high knowledge in Python to be done.
Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.
Bookings, Prices and Enquiries
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