Lecture
Deep Learning
Related lectures (64)
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Deep Learning Fundamentals
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Deep Learning Fundamentals
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Multilayer Perceptron: Training and Optimization
Explores the multilayer perceptron model, training, optimization, data preprocessing, activation functions, backpropagation, and regularization.
Kernel Methods: Neural Networks
Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.
Deep Learning: Data Representations and Neural Networks
Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.
Deep Learning: Data Representations and Neural Networks
Explores data representations, histograms, neural networks, and deep 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.
Neural Networks: Training and Optimization
Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.