Syllabus
Unit 1
Introduction to autonomous driving-Autonomous driving technologies-Autonomous driving algorithms – Perception in autonomous driving – Deep learning in autonomous driving perception -The Neuron – Feed- Forward Neural Networks – Linear neurons and their limitations Activation functions – Training feed forward neural networks – Gradient descent – Delta rule and learning rates – Backpropagation algorithm – Stochastic and minibatch gradient descent – Preventing overfitting – Momentum-Based optimization- Learning rate adaptation.
Unit 2
Convolutional Neural Networks (CNN) architecture – Accelerating training with batch normalization – Visualizing learning in convolutional networks – Embedding and representation learning – Autoencoder architecture – Denoising – Sparsity in autoencoders Introduction to GAN : Examples in Automotive Domains
Unit 3
Models for sequence analysis, Recurrent Neural Networks – Vanishing gradients – Long Short – Term Memory (LSTM) Units – Augmenting Recurrent networks with Attention – Deep Generative Networks – Generative Adversarial Networks – Deep Reinforcement Learning – Explore versus Exploit – Policy versus Value learning – Q-Learning and Deep Q-Networks Introduction to Generative AI and Federated Learning: Examples in Automotive Domains.
Text Books / References
References
- Nikhil Buduma, Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms, O’Reilly, 2017.
- Shaoshan Liu, Liyun Li, Jie Tang, Shuang Wu and Jean-Luc Gaudiot. Creating Autonomous Vehicle Systems, Morgan & Claypool Publishers,2018.
- Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.
- Aurélien Géron, Hands-On Machine Learning with Scikit- Learn and TensorFlow, O’Reilly, 2017.
- Nikhil Ketkar, Deep Learning with Python: A Hands-on Introduction, Apress, 2017.
