Computer Vision for Biodiversity Monitoring

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Explores challenges and opportunities in vision-based robotic perception, covering topics like SLAM, place recognition, event cameras, and collaborative visual intelligence.

Social Media Images: Understanding Food Perception with Deep Learning

Explores deep learning with Instagram images, understanding food perception, obesity, and mental health, and discusses the impact of social media images and ephemeral platforms like Snapchat.

PyTorch and Convolutional Networks

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

Transformers: Unifying Machine Learning Communities

Covers the role of Transformers in unifying various machine learning fields.

Learning Agents: Exploration-Exploitation Tradeoff

Explores the exploration-exploitation tradeoff in learning unknown effects of actions using multi-armed bandits and Q-learning.

Deep Learning Fundamentals

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Social Image Systems: From Past to Present

Explores the evolution of social image systems, deep learning models, selfies, and biometrics in online platforms.

Cloud computing support for IoT

Explores the evolution of cloud computing to support IoT applications and the challenges faced by deep learning algorithms in processing data from smart sensors.

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 Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.