Neural Networks and Deep Learning

This course (6 CFUs) includes two modules of 3 CFUs each: the first module focuses on the theoretical foundations of neural networks and deep learning, while the second module covers more practical and implementation issues.

*** The first lecture is scheduled on January 14, 2020 at 9:00, Gray Room, TeCIP Institute.


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Course Program

Course topics: theoretical part

  1. Introduction to neural computing
  2. Hopfield networks
  3. Unsupervised learning
  4. Reinforcement learning
  5. Supervised learning
  6. Towards Deep Neural Networks (DNNs)
  7. DNN models
  8. Convolutional Neural Networks
  9. DNNs for object classification
  10. DNNs for object detection
  11. Recurrent Neural Networks
  12. Deep Reinforcement learning
  13. Generative Adversarial Networks
  14. Sample applications and open issues

Course topics: practical part

  1. Coding neural networks from scratch in C
  2. Sample implementations of common neural networks
  3. Common frameworks for training and inference
  4. Examples of DNN implementations in Tensorflow and Caffe
  5. Simulation environments for control
  6. The OpenAI Gym framework
  7. The MuJoCo environment and related applications
  8. Genetic algorithms for reinforcement learning
  9. Accelerating DNNs on GPGPUs
  10. The NVIDIA TensorRT framework
  11. Real-time object detection from a video camera
  12. Accelerating deep networks on FPGA
  13. A sample implementation on the Zynq platform.