Deep Learning Agents: Reinforcement Learning

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Deep Reinforcement Learning: Proximal Policy Optimization Techniques

Covers deep reinforcement learning techniques for continuous control, focusing on proximal policy optimization methods and their advantages over standard policy gradient approaches.

Mini-Batches in On- and Off-Policy Deep Reinforcement Learning

Explains the significance of mini-batches in Deep Reinforcement Learning and the differences between on-policy and off-policy methods.

Foundations of Deep Learning: Transformer Architecture Overview

Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.

Machine Learning for Solving PDEs: Random Feature Method

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

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.

General Introduction into Artificial Neural Networks: part 3

Covers learning by rewards in deep reinforcement learning without math details.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

Deep Learning for Autonomous Vehicles: Predictive Models

Explores predictive models and trackers for autonomous vehicles, covering object detection, tracking challenges, neural network-based tracking, and 3D pedestrian localization.

First steps toward deep reinforcement learning

Explores the shift to deep reinforcement learning through neural networks for direct policy learning, bypassing Q-values and V-values.