![Title image: An Introduction To Deep Learning](images/title.png) ## Course Information ## A survey class of neural network implementation and applications. Topics include: optimization - stochastic gradient descent, adaptive and 2nd order methods, normalization; convolutional neural networks - image processing, classification, detection, segmentation; recurrent neural networks - semantic understanding, translation, question-answering; cross-domain applications - image captioning, vision and language. ### Instructor ### Joseph Redmon - Email: pjreddie@cs.washington.edu - Class: Tu/Thur 10:00-11:20 am, [Kane](https://www.washington.edu/maps/#!/kne) 120 #### Note: Our classroom has moved to Kane 120, not in CSE2 #### ### TAs ### Samuel Ainsworth - skainswo@cs.washington.edu Ivan Montero - ivamon@cs.washington.edu Prashant Rangarajan - prashr@cs.washington.edu Tobias Rohde - tobiasr@cs.washington.edu ### Office Hours ### - Monday: - Joe, 10am-11am via [Zoom](https://washington.zoom.us/j/3362756951) - Tuesday: - Ivan, 4pm-5pm in Allen 4th Floor Breakout Area - Wednesday: - Samuel, TBD - Thursday: - Prashant, 2pm-3pm via [Zoom](https://washington.zoom.us/j/95033420579) - Friday: - Tobias, 10am-11am in Gates 150 this week, Gates 131 after this week ### Resources ### - Ed Discussion Board: https://edstem.org/us/courses/14938/discussion/ - Canvas: https://canvas.uw.edu/courses/1477508 - Zoom: https://washington.zoom.us/j/99114176043 ## Homeworks ## - [Homework 0: Neural Networks](https://github.com/pjreddie/uwnet/blob/master/hw0.md) Due Monday October 18th. ## Final Project: ## There is a final project worth 20% of the final grade. ## Lectures ## #### Lecture 1: Machine Learning Review - [Slides](https://docs.google.com/presentation/d/18Hwleyj4aOX53KOoS8hOW0jLEcI3GIfodguToMK1BEI/edit?usp=sharing) #### Lecture 2: Neural Networks and Optimization - [Slides](https://docs.google.com/presentation/d/1ktTiLEPLnG4jr1MFpk0qczdfXyA3rDBu2Sv7v1YAAQ0/edit?usp=sharing) #### Lecture 3: Training Neural Networks - [Slides](https://docs.google.com/presentation/d/1wvz_SrFdFf0PV53ZVNxoz1dGDWU3dWrQoZQvoW_-ZHc/edit?usp=sharing) #### Lecture 4: Convolutional Neural Networks - [Slides](https://docs.google.com/presentation/d/1nhqXbYITrKeW_m1y-aEl7kPunhtxD2dtVosfkzGGA6k/edit?usp=sharing) #### Lecture 5: Image Classification - [Slides](https://docs.google.com/presentation/d/1L_v6K2ZxibIfWwJj7R7OcGyo3EPX6w3F5Ph-UV1lswI/edit?usp=sharing) ## Course Policies ## - Collaboration is encouraged! Feel free to discuss howemork and class material with other students. However, make sure you understand the concepts. Each student will complete and submit their own work. Do not directly or indirectly copy other students' work. - If you are working together or helping another student, work on teaching them concepts and answering general questions, not directly telling them what code to write. You're all smart; you should understand the line between productive collaboration and giving someone answers. - **For homeworks you may work with one other student in full collaboration** (i.e. sharing code, etc). Both students should still understand and contribute approximately equally to the solution and please note who you work with on your homework submission either as a comment in the code or in canvas. - Each student has 8 penalty-free late days for the whole quarter. Beyond that, late submissions are penalized up to 10% per day late.