Developmental Perspective & Spatial Vision

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 3 of 3

Statistical Physics in Machine Learning: Understanding Deep Learning

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

Deep Learning: Exploring Vision and Language Transformers

Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.

Depth Perception: Motion Parallax & Stereoscopic Vision

Explores depth perception through motion parallax and stereoscopic vision, emphasizing the importance of viewpoint tracking in virtual reality.

Understanding Deep Learning

Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.

Image Recognition: Datasets and Algorithms

Explores a 2019 paper on image recognition, dataset challenges, biases, and the impact of large-scale datasets on deep learning models.

Machine Learning Fundamentals

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

Computer Vision History Recap

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

Deep Learning Fundamentals

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Deep Learning: Convolutional Neural Networks and Training Techniques

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

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.