I am currently trying to do something like Mari/o and just wondered if anyone had any great podcasts I could listen to that could help develop my understanding of Tensorflow/Python/DQN and etc. Thanks in advance:)

Leslie D'Monte is a journalist with over two decades of experience, specializing in technology and science editing and writing. He has worked with leading media groups like HT Media Ltd. (Mint), Business Standard, The Times of India, The Indian Express, Jasubhai Media group and ZdNet India, both as a reporter and editor. Leslie is currently the National Technology Editor of Mint, the business publication of HT Media Ltd, and also a part of Mint's Leadership team. Leslie talks about the rebirth of artificial intelligence and gives his take on what impacts it is going to have on life as we know it. He also addresses some myths surrounding AI and machine learning.

Just FYI: "Judea Pearl created the representational and computational foundation for the processing of information under uncertainty. He is credited with the invention of Bayesian networks,..." His wiki page doesn't list his contributions, it links to other people's summaries of his contributions. The guy you say is stuck in an old paradigm is one of the main inventors of the new paradigm.

After repeatedly failing to gain traction in the music streaming space, Google's latest attempt is finally here. YouTube Music, first reported last month, is set to launch Tuesday and replace Google Play Music as the company's music subscription service. Some of its features might look awfully familiar to users of Spotify or Apple Music. YouTube Music, for example, borrows its pricing model from Spotify with a free tier to attract new potential subscribers to the platform and a $9.99 per month paid tier that offers ad-free listening, music downloads, and the ability to play music in the background on your mobile device. While both Spotify and Apple Music aim to intelligently learn from your listening habits to recommend songs you'll also like, Google takes this a step further.

"That job [to formalize ... the fundamental elements of human understanding] proved difficult and was never finished. A.I. researchers need to return to that project sooner rather than later, ideally enlisting the help of cognitive psychologists who study the question of how human cognition manages to be endlessly flexible. Today's dominant approach to A.I. has not worked out. If machine learning and big data can't get us any further than a restaurant reservation, even in the hands of the world's most capable A.I. company, it is time to reconsider that strategy.'

Firstly I just want to say I'm a teenager who's interested in Artificial Intelligence so my knowledge is limited/ slightly inaccurate. Sorry if this already exists/has been discussed. Do you see the possibility of deploying artificial intelligence in a simulated world in order to observe how they learn/grow/respond to events around them. Would this be a good way to teach them about the world before deploying them in the real world.

I am trying to reproduced result from the paper "DYNAMIC COATTENTION NETWORKS FOR QUESTION ANSWERING" (https://arxiv.org/abs/1611.01604). I have implemented the code in pytorch but it is overfitting. In the paper it is mention that the authors use dropout for regularization. I added dropout and it helps a bit but not too much. I am also curious if it is possible to get feedback on my model code.

I am by no means a particularly good example of study habits, but generally I tend to read what I need and go from there... Basically this in practice often means starting somewhere relevant to whatever work/assignment/project I'm trying to do, and then going backwards building a recursive stack of readings that seem important to understanding the previous thing until I reach a point where I am familiar with the material already. Then I work through the stack until I'm back to wherever I started. Essentially this is the backward chaining algorithm. I also, if I need to learn a lot from a book for some reason (i.e. a course) or have no particular goal in mind but find my self with a text that piques my interest, then I tend to skim from cover to cover everything that actually attracts my attention, occasionally flipping back to something that I realize is important for understanding later stuff. If it seems especially critical and I can't understand it, then I'll look through exercises and maybe do them if it seems worthwhile.

I am aware of several awesome Python libraries that allow me to do image manipulation, cropping, etc. to prepare my data for training input to Neural Nets. Are you aware of nice looking tools with a graphical interface specifically for the purpose of data preprocessing (resize, crop, brightness) and manipulation (say, assign classes to multiple images easily)? I know every problem is unique and everybody has its own setup, but are there tools out there that do this well?