Lectures related to Image Recognition: Datasets and Algorithms

Computer Vision: Historical Insights and Project Inspirations

Explores the historical development of computer vision and inspires innovative project ideas.

Deep Learning: No Free Lunch Theorem and Inductive Bias

Covers the No Free Lunch Theorem and the role of inductive bias in deep learning and reinforcement learning.

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

NFNets: Removing BatchNorm for High-Performance Image Recognition

Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.

Machine Learning Fundamentals

Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.

Deep Learning: Convolutional Neural Networks and Training Techniques

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Graph Search: Neural Networks and Deep Learning

Delves into graph search, neural networks, and deep learning, covering topics like convolutional neural networks and artificial neural networks.

Machine Learning for Feature Extraction

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Computer Vision History Recap

Offers a historical overview of computer vision, exploring key developments and influential figures in the field.

Visual Intelligence: Machines and Minds

Explores visual intelligence, image formation, computer vision, and representation understanding in machines and minds.

Machine Learning Fundamentals

Covers fundamental principles, opportunities, and challenges in machine learning.

PyTorch and Convolutional Networks

Covers PyTorch tensor data structure and training a CNN to classify images.

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.

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Edge Detection: Deep Learning Insights

Explores the evolution of edge detection techniques, from Canny to deep learning insights.

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.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Hand Pose Estimation

Covers hand pose estimation, regression techniques, and the evolution of image classification models from LeNet to VGG19.

Deep Learning: Theory and Applications

Explores the mathematics of deep learning, neural networks, and their applications in computer vision tasks, addressing challenges and the need for robustness.

Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.