Lecture

Environmental Image Analysis

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Related lectures (64)

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Environmental Engineering Journey

Explores Valérie's path from science to environmental engineering at EPFL, focusing on current environmental challenges and advanced projects in data processing and artificial intelligence.

Reinforcement Learning: Q-Learning

Covers Q-Learning in reinforcement learning, exploring action values, policies, and the societal impact of algorithms.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Machine Learning at EPFL

Explores the EPFL Alice unit's role in shaping machine learning and AI in Europe, focusing on research advancements and collaboration within the AI community.

Cloud computing support for IoT MOOC: IoT Systems and Industrial Applications with Design Thinking

Explores the evolution of cloud computing to support IoT applications and the challenges faced by deep learning algorithms in processing data from smart sensors.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Generalization in Deep Learning

Explores generalization in deep learning, covering model complexity, implicit bias, and the double descent phenomenon.

AI for Science

Explores fundamental principles in scientific research, the impact of computers, numerical algorithms, and deep learning in solving high-dimensional problems.

Autonomous Vehicles: Intelligence and Perception

Explores intelligence, perception, and AI applications in autonomous vehicles, emphasizing rational thinking and social intelligence.

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Intelligent Systems for Health and Medicine

Explores AI in Health and Medicine, predicting heart attacks and detecting skin cancer.

Social Media Images: Understanding Food Perception with Deep Learning

Explores deep learning with Instagram images, understanding food perception, obesity, and mental health, and discusses the impact of social media images and ephemeral platforms like Snapchat.

Deep Learning: Fundamentals

Covers the fundamentals of deep learning, from artificial neurons to modern networks.

Non-Conceptual Knowledge Systems

Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.

Deep Learning Building Blocks

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

Document Segmentation: dhSegment

Explores the development and improvements of dhSegment, an open-source document segmentation package using PyTorch.

Artificial Intelligence: The New Radical Addition to Work

Explores the convergence of IoT, Big Data, and Deep Learning in Artificial Intelligence applications across various industries.

Convolutional Neural Networks

Introduces Convolutional Neural Networks, covering fully connected layers, convolutions, pooling, PyTorch translations, and applications like hand pose estimation and tubularity estimation.

Deep Learning: Edge Detection and Neural Networks

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