Category: Convolutional Neural Networks

The video lectures for Stanfords very popular CS231n (Convolutional Neural Networks for Visual Recognition) that was held in Spring 2017 was released this month. (According to their twitter page, the cs231n website gets over 10 000 views per day. The reading material on their page is really good at explaining CNNs)

Here are the video lectures:

• Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition
• Lecture 2 | Image Classification
• Lecture 3 | Loss Functions and Optimization
• Lecture 4 | Introduction to Neural Networks
• Lecture 5 | Convolutional Neural Networks
• Lecture 6 | Training Neural Networks I
• Lecture 7 | Training Neural Networks II
• Lecture 8 | Deep Learning Software
• Lecture 9 | CNN Architectures
• Lecture 10 | Recurrent Neural Networks
• Lecture 11 | Detection and Segmentation
• Lecture 12 | Visualizing and Understanding
• Lecture 13 | Generative Models
• Lecture 14 | Deep Reinforcement Learning
• Lecture 15 | Efficient Methods and Hardware for Deep Learning
• Lecture 16 | Adversarial Examples and Adversarial Training

 

These are the assignments for the course:

• Assignment #1: Image Classification, kNN, SVM, Softmax, Neural Network
• Assignment #2: Fully-Connected Nets, Batch Normalization, Dropout, Convolutional Nets
• Assignment #3: Image Captioning with Vanilla RNNs, Image Captioning with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks

 

Also Make sure to check out last years student reports.  note: one is about improving the state of the art of detecting the Higgs Boson.