Lecture
Fully Connected Networks on MNIST and SUSY Datasets
Related lectures (87)
PyTorch and Convolutional Networks
Covers PyTorch tensor data structure and training a CNN to classify images.
Semiconductor Devices II: Contact Resistance Modeling
Explores contact resistance modeling in semiconductor devices, focusing on gate voltage calculation and defect analysis.
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Vision-Language-Action Models: Training and Applications
Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.
Binary Sentiment Classifier Training
Covers the training of a binary sentiment classifier using an RNN.
Introduction to Data Science
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Deep Learning: Convolutional Neural Networks
Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.
Deep Learning Building Blocks
Covers tensors, loss functions, autograd, and convolutional layers in deep learning.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Deep Learning for Autonomous Vehicles: Learning
Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.