MIT Deep Learning
MIT Deep Learning is an expansive repository that hosts a variety of tutorials designed to accompany the illustrious deep learning courses offered by MIT. The project serves as an educational tool to help learners dive into the world of deep learning through hands-on experience and practical demonstrations.
Deep Learning Basics
The "Deep Learning Basics" tutorial sets the stage for anyone new to the field. It ties closely with the accompanying lecture, delineating fundamental concepts like feedforward neural networks and convolutional neural networks. This tutorial is well-suited for beginners, providing both a Jupyter Notebook and a Google Colab version to allow for interactive learning. Additionally, a blog post and a lecture video are available to supplement the learning experience.
Driving Scene Segmentation
This tutorial offers an in-depth look at semantic segmentation, an advanced computer vision task. Using a state-of-the-art model known as DeepLab, learners can practice segmenting a video sample from the MIT Driving Scene Segmentation Dataset. Like the basics tutorial, this one is available on both Jupyter Notebook and Google Colab for interactive exploration.
Generative Adversarial Networks (GANs)
The tutorial on Generative Adversarial Networks (GANs) unveils the captivating process of generative modeling, specifically focusing on BigGAN, a cutting-edge conditional GAN model. This resource is perfect for those interested in the intersection of neural networks and creativity, providing practical insights into how machines can generate new data.
DeepTraffic Deep Reinforcement Learning Competition
Diving into the realm of reinforcement learning, the DeepTraffic competition challenges participants to craft neural networks aimed at optimizing vehicle navigation through congested highway traffic. This competition provides a platform for learners to apply deep reinforcement learning concepts in a competitive and practical setting, offering a plethora of learning materials and a chance to push the boundaries of what's possible with AI.
Team
The project is driven by a dedicated team of experts and researchers, including Lex Fridman, Li Ding, Jack Terwilliger, Michael Glazer, Aleksandr Patsekin, Aishni Parab, Dina AlAdawy, and Henri Schmidt. Their collective expertise propels the MIT Deep Learning initiative, making cutting-edge AI education accessible to a global audience.