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Lecture 7: Convolutional Neural Networks

Lecture 7: Convolutional Neural Networks Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - 1 27 Jan 2016. Administrative A2 is due Feb 5 (next Friday). Project proposal due Jan 30 (Saturday). - ungraded, one paragraph - feel free to give 2 options, we can try help you narrow it - What is the problem that you will be investigating? Why is it interesting? - What data will you use? If you are collecting new datasets, how do you plan to collect them? - What method or algorithm are you proposing? If there are existing implementations, will you use them and how? How do you plan to improve or modify such implementations? - What reading will you examine to provide context and background? - How will you evaluate your results? Qualitatively, what kind of results do you expect ( plots or figures)? Quantitatively, what kind of analysis will you use to evaluate and/or compare your results ( what performance metrics or statistical tests)?

Forward prop it through the graph, get loss 3. Backprop to calculate the gradients 4. Update the parameters using the gradient. ... spatial locations activation maps 1 28 28 consider a second, green filter. Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 7 - 16 27 Jan 2016 32 32 3

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