Robottle: Autonomous Robot Design

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Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Multi-layer Neural Networks

Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.

Neural Networks: Hierarchical Models and Odor Taxis

Covers neural function, hierarchical models, odor taxis behaviors, and disparate circuit parameters in 18 slides.

Neural Networks: Training and Optimization

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Motion Planning: Social & Ethical Awareness

Explores motion planning for autonomous vehicles with a focus on social and ethical considerations.

Neural Networks: Two-layer Networks and Backpropagation

Explores two-layer neural networks and backpropagation for learning feature spaces and approximating continuous functions.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.