Advanced Deep Learning

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
• Deep Belief Nets, Stacked RBMs
• Applications to NLP, Pose and Activity Recognition in Videos
• Recent Advances
• Large-Scale Learning
• Neural Turing Machines