Making the most out of Part 2 v2

Part 2 & Alumni (2018)
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If you have some extra time, then these 3 papers would be a great starter kit for object detection being discussed in today’s lecture:

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So here I am, continuing my plan to post blogs every week on topics covered/related to lessons. Though this one is unrelated to todays or last weeks lesson, let me know your thoughts :slight_smile:

Convolutional Neural Network - II

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Would be really interesting to see professional grade equivalents of Jeremy’s notebooks in tf+keras.

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@snagpaul Yeah, I agree, but at this pace it’s going to be tough! Today’s lecture was quite packed by itself! I’m very intrigued by feature pyramids, though… Really looking forward to lectures 13-14, as usual those will be the best ones!
From my point of view (working in Computer Vision) today’s lecture has been quite the best of the Pytorch timeline! SSD+Yolo easily explained, debugged and implemented in 2.5 hours… Amazing.

Really envious of all of the people that are following in person! And, BTW, congratulations @binga for your incredible efforts!

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I have been working through yesterday’s lesson and writing my own notes along side the notebook. Getting some sense of satisfaction on the conceptual level following this strategy :smile:

Have already worked through the notebook in my own pace and am able to absorb what and how things are being down, but unable to reproduce the same independently.

I have read @radek’s and other people posting about how much efforts and time it takes to reach that level of confidence, and I still find myself thinking about how to reach there a bit faster. :thinking:

Any pointers in addition to practice that you can suggest? @radek

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“Every day I sit on a sofa toward my dream”

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is all there is

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Found an interesting collection dataset for deep learning:

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Don’t use @ (people don’t like it)
Just pose a query and someone will surely respond to resolve

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Will keep that in mind. Thanks :slight_smile:

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There are good and bad uses of @ . In this case he was saying he liked another student’s writing, so it seems nice to at-mention that student so they know about the praise! :slight_smile:

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So until today’s lecture, I was able to read the YOLO and SSD paper. Currently preparing a summary for the same, will be posting it soon on Medium. :smile:

For reading papers it is very useful if we set a goal for our reading in mind before starting out. The goal can be to understand the model architecture or to clarify any novel concept introduced in the paper. I found this 3 step strategy very efficient:

  1. Read the Abstract and Introduction carefully. Just skim over all other section headings and bold highlights. Do not even look at the expressions in the first go.
  2. Compare your initial impression of the paper by reading related blog posts and explanations online.
  3. Now give a thorough read to the paper by working through expressions and highlighting any major points which let you achieve your goal in a better manner.

I found this video by Siraj is really useful .

Alot more to cover after today’s lesson. Really tough to learn all the neat tricks and new ideas explored today, without having a good grip over NLP practice.

Planning to dedicate 1 whole day to practice NLP from basics using these resources:

Another go at the IMDB lesson 4 from Part I, will surely accelerate learning for this week. :slight_smile:

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I just tried something that helped me understand Part2 v2 better - used data augmentation!

I started watching Part1 v2 videos as well, and having similar things told in two different ways helped me understand the concepts and generalize better. We can see how things have changed in the past year… why we’re doing what we’re doing now… and how we got here.

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