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- 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
Good understanding of Machine Learning. At least theoretical knowledge of Deep Learning.
The global overview of deep learning.
The exercises are sufficiently practical and do not need high knowledge in Python to be done.
Doing exercises on real examples using Eras. Italy totally understood our expectations about this training.
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Deep Learning for Vision with Caffe
Deep Learning with Keras
Deep Learning for Self Driving Cars
TensorFlow Lite for Embedded Linux
TensorFlow Lite for Android
TensorFlow Lite for iOS
Tensorflow Lite for Microcontrollers