MIT Deep Learning Basics: Introduction and Overview

23 thoughts on “MIT Deep Learning Basics: Introduction and Overview”

  1. this guy is a fraud. He clearly doesn't know the subject matter, he is just reading off his slides. lame, sad

  2. Deep learning can't tell a fucking dog from an ostrich and then musk wants to drive a car. fucking that company is doomed. All the fancy talk about robotax is 100,000 years away.

  3. how much probability and statistics is necessary for machine learning ? why most of the people ignore its application in machine learning?

  4. It mgiht be horrible be in this class. The subject seems dense and interesting, but the lecture is bad (skimming thru ppt slides), altough one can assume the lecturer has large knowledge on the subject.

  5. I am glad I am getting old. I would hate to live in this world for much longer. Complete alienation……

  6. Thank you so much Lex. This will help us a lot. This will help the students, who cant afford paid online courses and none in the neighbourhood can teach.

  7. Top 3 deep learning books that will take you to the ultimate level. If you go through these books then definitely you will become a deep learning expert.

    1. Deep Learning (Adaptive Computation and Machine Learning series) Hardcover – 3 Jan 2017
    by Ian Goodfellow (Author), Yoshua Bengio (Author), Aaron Courville (Author), Francis Bach (Author). You can get it from Amazon at below link.

    https://amzn.to/2IuJmHC

    2. Deep Learning: A Practitioner's Approach Paperback – 2017

    by Josh Patterson (Author), Adam Gibson (Author). You can purchase it from Amazon at below link.

    https://amzn.to/2Vdbyoe

    3. Hands-On Machine Learning with Scikit-Learn and Tensor Flow: Concepts, Tools, and Techniques to Build Intelligent Systems Paperback – 2017 by AurÈlien GÈron (Author). You can purchase it from Amazon at below link.

    https://amzn.to/2DlkGgA

  8. The notion that humans only need small number of examples to be able to learn to solve a complex problem is wrong. This statement may seem true at first, specially in the context of how we view traditional learning, however in neurosciences this notion is being dispelled through the accumulation of evidence around learning during sleep. Sleep wave spindles allow humans to replay information acquired throughout the day at many times the speed and many times over during the night. Random twitches that a baby generates during sleep are reinforced thousands of times during sleep wave cycles (probably why babies actually require such a large amount of sleep). Although it may be true that humans need relatively many less training examples than do artificial learning algorithms, it is often misunderstood and we often assign superhuman capacity to ourselves. I think once we are able to find an algorithm to generate a sleep like pattern of learning and waking and the ability for a learning algorithm to "dream up" its own learning cases or put a spin on the information it acquired during the day, we will make major strides towards a much more general AI.

  9. R Deep Learning Essentials: A step-by-step guide to building deep learning models using TensorFlow, Keras

    https://amzn.to/2HMyCnm

    (This is an affiliate link)

  10. Great lecture very informative! However this makes me believe that we are very far from a true Artificial General A/I. I watched your interview with Elon, and he is very concerned with A. General A/I but IMO unless we go with a brute force hardware approach I doubt we will ever be able to create a General A/I

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