Lecture
Robustness and Diffusion Models
Related lectures (34)
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Data-Driven Modeling in Neuroscience: Meenakshi Khosla
By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Kernel Methods: Neural Networks
Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.
Neural Networks: Training and Optimization
Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.
Neural Networks: Deep Neural Networks
Explores the basics of neural networks, with a focus on deep neural networks and their architecture and training.
Deep Learning Fundamentals
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Recurrent Neural Networks: Language Detection
Explores language detection using Recurrent Neural Networks and supervised learning concepts.
Neural Networks: Training and Optimization
Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.
Deep Learning: Data Representations and Neural Networks
Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.