Guest post by MIT 6.S191 Introduction to Deep Learning MIT 6.S191: Introduction to Deep Learning is an introductory course offered formally at MIT and open-sourced on its course website. The class consists of a series of foundational lectures on the fundamentals of neural networks and their applications to sequence modeling, computer vision, generative models, and reinforcement learning.MIT's offi…

Sign in to report inappropriate content. MIT Introduction to Deep Learning 6.S191: Lecture 1 *New 2019 Edition* Foundations of Deep Learning Lecturer: Alexander Amini January 2019 For all lectures, slides and lab materials: http://introtodeeplearning.com

For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.

This is lecture 1 of course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. Links to individual lecture videos for the course: Lecture 1: Introduction to Deep Learning and Self-Driving Cars https://youtu.be/1L0TKZQcUtA Lecture 2: Deep Reinforcement Learning for Motion Planning https://youtu.be/QDzM8r3WgBw Lecture 3: Convolutional Neural Networks for End-to-End Learning of the Driving Task https://youtu.be/U1toUkZw6VI Lecture 4: Recurrent Neural Networks for Steering through Time https://youtu.be/nFTQ7kHQWtc Lecture 5: Deep Learning for Human-Centered Semi-Autonomous Vehicles https://youtu.be/ByZF8_-OJNI

MIT 6.S191 is more than just another lecture series on deep learning. In designing the course, we wanted to do something more. We wanted to equip our audience with the practical skills necessary to go out and implement their own deep learning models, to apply what they got out of this course to the questions that excite and inspire them. And so, we turned to TensorFlow. We designed two TensorFlow based software labs, focusing on music generation with recurrent neural networks and pneumothorax detection in medical images, to complement the course lectures.

Simple, someone has done the homework and checked Kurzweil's predictions against reality. At best, I think he is not better than 50/50. Importantly, his methodology is quite simple, too. If anyone cares to, I don't personally think it's something that is beyond an above-average person's capability.

This class is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application. MIT 6.S094: Deep Learning for Self-Driving Cars is a course on a cutting-edge research area. Support for this course was genorously provided by the companies whose logos are shown below. And none of it would be possible without the great community of bright young minds at MIT and beyond.