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Nuts and Bolts of Applying Deep Learning (Andrew Ng)



Lex Fridman

The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that’s useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) and full live streams below.

Having read, watched, and presented deep learning material over the past few years, I have to say that this is one of the best collection of introductory deep learning talks I’ve yet encountered. Here are links to the individual talks and the full live streams for the two days:

1. Foundations of Deep Learning (Hugo Larochelle, Twitter) – https://youtu.be/zij_FTbJHsk
2. Deep Learning for Computer Vision (Andrej Karpathy, OpenAI) – https://youtu.be/u6aEYuemt0M
3. Deep Learning for Natural Language Processing (Richard Socher, Salesforce) – https://youtu.be/oGk1v1jQITw
4. TensorFlow Tutorial (Sherry Moore, Google Brain) – https://youtu.be/Ejec3ID_h0w
5. Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU) – https://youtu.be/rK6bchqeaN8
6. Nuts and Bolts of Applying Deep Learning (Andrew Ng) – https://youtu.be/F1ka6a13S9I
7. Deep Reinforcement Learning (John Schulman, OpenAI) – https://youtu.be/PtAIh9KSnjo
8. Theano Tutorial (Pascal Lamblin, MILA) – https://youtu.be/OU8I1oJ9HhI
9. Deep Learning for Speech Recognition (Adam Coates, Baidu) – https://youtu.be/g-sndkf7mCs
10. Torch Tutorial (Alex Wiltschko, Twitter) – https://youtu.be/L1sHcj3qDNc
11. Sequence to Sequence Deep Learning (Quoc Le, Google) – https://youtu.be/G5RY_SUJih4
12. Foundations and Challenges of Deep Learning (Yoshua Bengio) – https://youtu.be/11rsu_WwZTc

Full Day Live Streams:
Day 1: https://youtu.be/eyovmAtoUx0
Day 2: https://youtu.be/9dXiAecyJrY

Go to http://www.bayareadlschool.org for more information on the event, speaker bios, slides, etc. Huge thanks to the organizers (Shubho Sengupta et al) for making this event happen.

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45 thoughts on “Nuts and Bolts of Applying Deep Learning (Andrew Ng)
  1. Why the white-board is "all-white"? DO these big organizers care for these basic details??
    With all the intelligence expected from machines, what happened to the responsibility and common sense of the human brains?

  2. Hereby I declare myself as the biggest fan of Ng. He has this amazing capability of simplifying the most complex equations. Hats off to you sir. !!

  3. ANSWER TO ETHICS CAR CRASH DEBATE = If a crash is eminent to have a fatality and AI must make a decision to kill x or yz people. answer = AI hacks x and yz units and forces them to move. Should anti-AI trust software prevent AI from moving x or/& yz, it was x or/& yz 's persons conscious fault and decision. NOT AI's

  4. Very nice practical lecture. About his rule of thumb though, I don't think our doctors decide on cancer image in 1 second. Is he underpromising, over delivering?

  5. At the end of the talk about how to get better at machine learning. Andrew states we should read more papers (add more data), and work at it for longer periods of time (train the model longer). Literally the same stuff for the algorithm improvements….

  6. 30:16 yo ng Andrew, what do you think about angles distortion alongations zoom. ? 35:49

    think about a bird in the air with airplane above it. SID OID IMAGE OBJECT Distance IOD TGB OID
    they might look the same size in appearance. This example puts physiology and pereception in the equation, with thinking, (nervous system) understanding. comprehenstion to be discovered. TGB
    instead of thinking technology, think about a bird in the air, with a shine on it. What do you think about a hawk that looks gold in the air flying in circles, what is the energy what perception do we use from it?TGB, we are the most brilliant thing on the planet the best part of each animal so to speak.

    Also for the google team, (the spell check guys or whatever your called)
    Google, Zoom is created by OID regulation focus. SOCHASTIC distortion. would be away to describe being as boring as possible.

  7. At the begening of the video, Ng Andrew, goes through a couple of steps to attempt to separate him from the lesson. He then Seperates your outgoing attention , theres lots of sound stuff going on , intent of forced listening to gain access to . calculating (setting the tone) what volume is, where action to volume is. multiple sub division. or a spliced wire to be put back together (figured accordingly)TGB He is using the bilingual trick here though, I mean what can you say/whats to say, if you have it, your made not to use it, its going to surface in some platform. Lets make it good Andrew (chineese (Asian)pie sign TGB) I'm gonna put it on a grilled cheese sandwhichTGB

    But he knows its there, uses it uniformly in a specific place.

  8. He is telling the truth about whats happenning right at your face. He's a shill, he's mocking you. He's a deceiver! Don't trust him, believe in Christ only. He is the Truth.

  9. Machine learning looks promising. Still pretty narrow AI but it will be interesting to see if general machine learning is invented soon, maybe not in 2020 but who knows. Exponential progress!

  10. Andrew Ng looks so much like Andrew Yang.
    Soft voice, the ability to convey in simple terms to masses, openness. Awesome Guy
    Thank you for the upload 🙂

  11. I didn't understand at 27:23, if the training error is high then it's mean we are having high bias in training error. Please explain this point a little bit. Thanks

  12. Soft voice, the ability to convey in simple terms to masses, openness. Awesome Guy
    Andrew Ng is the best at teaching us machine learning , deep learning all the stuff. GURU

  13. Andrew Ng is my ML sensei
    HE has taught me so much on Machine Learning. We all owe him 🙂
    HE has taught me so much on Machine Learning. We all owe him 🙂

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