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Convolutional Neural Networks for Visual Recognition
Instructor: Fei-Fei Li
Department: Computer Science
Institution: Stanford University
Platform: Independent
Price: Free
Prerequisites: Machine Learning, Calculus, Linear Algebra, Probability and Statistics

1. Proficiency in Python, high-level familiarity in C/C++ All class assignments will be in Python (and use numpy) (we provide a tutorial here for those who aren't as familiar with Python), but some of the deep learning libraries we may look at later in the class are written in C++. If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Javascript) you will probably be fine. 2. College Calculus, Linear Algebra (e.g. MATH 19 or 41, MATH 51) You should be comfortable taking derivatives and understanding matrix vector operations and notation. 3. Basic Probability and Statistics (e.g. CS 109 or other stats course) You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc. 4. Equivalent knowledge of CS229 (Machine Learning) We will be formulating cost functions, taking derivatives and performing optimization with gradient descent.

Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. 2016.
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge.