Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Home

Lecture

Deep Learning Fundamentals

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (43)

Deep Learning: Edge Detection and Neural Networks Log in to Mediaspace to watch this video

Discusses edge detection techniques and the evolution of deep learning in neural networks.

Neural Networks: Training and Optimization Log in to Mediaspace to watch this video

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

Feed-forward Networks Log in to Mediaspace to watch this video

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.

Deep Neural Networks Log in to Mediaspace to watch this video

Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.

Deep Learning: Convolutional Networks Log in to Mediaspace to watch this video

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Deep Learning: Data Representations and Neural Networks Log in to Mediaspace to watch this video

Explores data representations, histograms, neural networks, and deep learning concepts.

Gradient Descent: Optimization Techniques Log in to Mediaspace to watch this video

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Neural Networks: Training and Optimization Log in to Mediaspace to watch this video

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.

Deep Learning: Convolutional Neural Networks and Training Techniques Log in to Mediaspace to watch this video

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

Linear Models for Classification: Logistic Regression and SVM Log in to Mediaspace to watch this video

Covers linear models for classification, focusing on logistic regression and support vector machines.

Graph Neural Networks: Interconnected World Log in to Mediaspace to watch this video

Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.

Gradient Descent: MNIST Dataset and Logistic Loss Log in to Mediaspace to watch this video

Focuses on implementing gradient descent with the MNIST dataset and logistic loss in machine learning.

Robust Vision Transformer: Patch Embeddings & RVT Blocks Log in to Mediaspace to watch this video

Covers bio-inspired navigation, graph convolutional networks, and robust vision transformer architectures for visual intelligence.

Cross-Entropy and Regression Cost Functions Log in to Mediaspace to watch this video

Explores cross-entropy in classification and various regression cost functions.

Regression: Simple and Multiple Linear Log in to Mediaspace to watch this video

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Calibration Curves in Quantitative Ligand Binding Assays Log in to Mediaspace to watch this video

Covers the importance of calibration curves in ensuring accurate sample quantitation in ligand binding assays.

Modern Regression: Smoothing and Modelling Choices Log in to Mediaspace to watch this video

Explores roughness penalty, band matrices, and Bayesian inference in regression smoothing.

Machine Translation: Attention Mechanism Log in to Mediaspace to watch this video

Explores the attention mechanism in machine translation, addressing the bottleneck problem and improving NMT performance significantly.

Regularization Techniques Log in to Mediaspace to watch this video

Explores regularization in linear models, including Ridge Regression and the Lasso, analytical solutions, and polynomial ridge regression.

Testing and Debugging Log in to Mediaspace to watch this video

Explores the history of computing, automated testing, TDD, test coverage, bug reports, and debugging.

Page 2 of 3