Lectures related to Torch (machine learning)

Generative Models: Trajectory Forecasting

Explores generative models for trajectory forecasting in autonomous vehicles, including discriminative vs generative models, VAES, GANS, and case studies.

Deep Learning for Autonomous Vehicles: Predictive Models

Explores predictive models and trackers for autonomous vehicles, covering object detection, tracking challenges, neural network-based tracking, and 3D pedestrian localization.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Infoscience Repository: EPFL Open Access Policy

Covers Infoscience, EPFL's repository for researchers' publications and the importance of open access principles, including submission processes and library activities.

Fully Connected Networks on MNIST and SUSY Datasets

Covers the implementation of fully connected neural networks on two datasets using PyTorch.

Scientific Machine Learning: Applications and Algorithms

Explores scientific machine learning applications, challenges with sparse data, and physics-inspired algorithms to improve spectral methods.

Deep Learning Building Blocks: Linear Layers

Explains the fundamental building blocks of deep learning, focusing on linear layers and activation functions.

Deep Learning: Convolutional Networks

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

Deep Learning for Autonomous Vehicles: Learning

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Scientific Machine Learning: Introduction to Spin Glass Models

Introduces Scientific Machine Learning, emphasizing its application in various scientific fields and the connection between machine learning and physics.

Self-supervised Learning for Autonomous Vehicles

Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.

Trajectory Forecasting in Autonomous Vehicles

Explores trajectory forecasting in autonomous vehicles, focusing on deep learning models for predicting human trajectories in socially-aware transportation scenarios.

Tensorial Properties in Scientific Machine Learning

Delves into predicting non-scalar properties beyond energies in scientific machine learning, focusing on dipole moments, polarizability, and dielectric response.