InfoCoBuild

6.S191 Introduction to Deep Learning

6.S191 Introduction to Deep Learning (January IAP 2020, MIT OCW). Instructors: Alexander Amini and Ava Soleimany. This is MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), and we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. (from ocw.mit.edu)

Lecture 09 - Deep CPCFG for Information Extraction

Go to the Course Home or watch other lectures:

2022 Lectures

Lecture 01 - Introduction

Lecture 02 - Recurrent Neural Networks and Transformers

Lecture 03 - Convolutional Neural Networks

Lecture 04 - Deep Generative Modeling

Lecture 05 - Reinforcement Learning

Lecture 06 - Deep Learning New Frontiers

Lecture 07 - LiDAR for Autonomous Driving

2021 Lectures

Lecture 01 - Introduction

Lecture 02 - Recurrent Neural Networks

Lecture 03 - Convolutional Neural Networks

Lecture 04 - Deep Generative Modeling

Lecture 05 - Reinforcement Learning

Lecture 06 - Deep Learning New Frontiers

Lecture 07 - Evidential Deep Learning and Uncertainty

Lecture 08 - AI Bias and Fairness

Lecture 09 - Deep CPCFG for Information Extraction

Lecture 10 - Taming Dataset Bias via Domain Adaptation

Lecture 11 - Towards AI for 3D Content Creation

Lecture 12 - AI in Healthcare

2020 Lectures

Lecture 01 - Introduction

Lecture 02 - Recurrent Neural Networks

Lecture 03 - Convolutional Neural Networks

Lecture 04 - Deep Generative Modeling

Lecture 05 - Reinforcement Learning

Lecture 06 - Deep Learning New Frontiers

Lecture 07 - Neurosymbolic AI

Lecture 08 - Generalizable Autonomy for Robot Manipulation

Lecture 09 - Neural Rendering

Lecture 10 - Machine Learning for Scent