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Lecture 16 | Adversarial Examples and Adversarial Training

In Lecture 16, guest lecturer Ian Goodfellow discusses adversarial examples in deep learning. We discuss why deep networks and other machine learning...

Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)

Lecture 15 | Efficient Methods and Hardware for Deep Learning

In Lecture 15, guest lecturer Song Han discusses algorithms and specialized hardware that can be used to accelerate training and inference of deep lea...

Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)

Lecture 14 | Deep Reinforcement Learning

In Lecture 14 we move from supervised learning to reinforcement learning (RL), in which an agent must learn to interact with an environment in order t...

Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)

Lecture 13 | Generative Models

In Lecture 13 we move beyond supervised learning, and discuss generative modeling as a form of unsupervised learning. We cover the autoregressive Pixe...

Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)

Lecture 12 | Visualizing and Understanding

In Lecture 12 we discuss methods for visualizing and understanding the internal mechanisms of convolutional networks. We also discuss the use of convo...

Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)

Lecture 11 | Detection and Segmentation

In Lecture 11 we move beyond image classification, and show how convolutional networks can be applied to other core computer vision tasks. We show how...

Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)

Lecture 10 | Recurrent Neural Networks

In Lecture 10 we discuss the use of recurrent neural networks for modeling sequence data. We show how recurrent neural networks can be used for langua...

Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)

Lecture 9 | CNN Architectures

In Lecture 9 we discuss some common architectures for convolutional neural networks. We discuss architectures which performed well in the ImageNet cha...

Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)

Lecture 8 | Deep Learning Software

In Lecture 8 we discuss the use of different software packages for deep learning, focusing on TensorFlow and PyTorch. We also discuss some differences...

Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)

Lecture 7 | Training Neural Networks II

Lecture 7 continues our discussion of practical issues for training neural networks. We discuss different update rules commonly used to optimize neura...

Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)

Lecture 6 | Training Neural Networks I

In Lecture 6 we discuss many practical issues for training modern neural networks. We discuss different activation functions, the importance of data p...

Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)

Lecture 5 | Convolutional Neural Networks

In Lecture 5 we move from fully-connected neural networks to convolutional neural networks. We discuss some of the key historical milestones in the de...

Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)