Author here! Deep Speech is an excellent repo if you just want to pip install something. We wanted to do a comprehensive writeup to give devs the ability to build their own end-to-end model.

- Conversations with Tyler

- Deep Questions With Cal Newport

- CoRecursive - Coding Stories (This is my own podcast, so not a listener so much as a creator)

Deep Leaf Audio did a really excellent audiobook version of The Illuminatus Trilogy that adds some layers on top of the book itself. I usually don't bother with audiobooks -- it's slower and more effort than reading -- but with that particular book it's worthwhile.

If anyone else here is working on Deep Learning on/with vector graphics, feel free to ping me.
I am not affiliated with the great work linked here - but I have been working on the general topic as a side project for a few years.

Man's Search for Meaning expanded my view of the human spirit and life in general.

Deep Work gave me some good insight on how to get the most out of my days.

Sapiens vastly widened and shifted my understanding of the myths that make up our society.

Almost agree to what you said.
Just a piece of more context: the 1st author wrote about connection between physics's Renormalization Group and Deep learning which was featured in [1] 4 years ago.

[1]: https://www.quantamagazine.org/deep-learning-relies-on-renor...

Full title: Deep Learning for Programmers: An Interactive Tutorial with CUDA, OpenCL, MKL-DNN, Java, and Clojure

Based on Uncomplicate open source libraries https://github.com/uncomplicate

I was looking for something that would answer common questions for some time, but couldn't find anything. Pointing students to research papers unfortunately doesn't work in most of the cases. Deep learning book is too hard for some and they want to see a comparison of different sources.

HNers interested in this might also be interested in Deep Dive from Stanford CS Professor Chris Ré.

http://deepdive.stanford.edu/

I went the other way round; dumped all JetBrains stuff and now write most things in Atom/Nuclide. At least it's not as slow with very large files as JetBrains stuff. I realized with Deep Learning work and Jupyter Notebook that I actually don't need IDE anymore...

Related: Deep Neural Networks for YouTube Recommendations https://research.google.com/pubs/pub45530.html (Discussion: https://news.ycombinator.com/item?id=12426064)

This echoes the sentiment of Deep Work, which helped me a lot with my own productivity.

If you can do 4 hours of productive work, truly, no distraction, deep productive work, then you're likely way ahead of your peers.

Leave the rest of your time for shallower work, which doesn't require intensity of focus, and make sure to spend some time re-charging, where you focus on not doing work or being connected at all.

Hi, I made Deep Grammar. I also wrote a blog post about it https://www.linkedin.com/pulse/deep-grammar-checking-using-l...

* Winter World: The Ingenuity of Animal Survival by Bernd Heinrich: A great pop-sci book about how animals adapt to extreme winter biomes

* Deep by James Nestor: A look at the extreme sport of freediving, where contestants train to submerge to depths much greater than 400ft without any oxygen and pressure equalizing equipment

* The Idea Factory: Bell Labs and the Great Age of American Innovation by John Gertner: A great book about the history of Bell Labs, the scientists and engineers that brought great innovations to society including phones

* Wizard: The Life and Times of Nikola Tesla by Marc Seifer: A biography of Nikola Tesla. Its quite astounding how much one person can achieve in their lifetime

Before I started a new job I had myself read:
1. So good they can't ignore you
2. Deep work
3. Mythical man-month
4. Power of habit

Now I am reading Tools of titans by Tim Ferris. I never thought I would get into the genre of self-improvement books, but it seems I like these :-) Even though I am conscious about the fact, that I am applying maybe 10% of the books advice :P

Another thing I am reading is Math from three to seven [1], mostly because I would like to discuss math with my 4yo daughter one day, preferably sooner rather than later, because I find math discussions immensely fun :-D Maybe I will even start a math-circle :-)

[1] http://www.msri.org/people/staff/levy/files/MCL/Zvonkin.pdf

Python Machine Learning is a great book to start the author made a great curriculum if you would like to follow it. https://sebastianraschka.com/faq/docs/ml-curriculum.html

This is mostly ML if you would like to dive into deep learning I think fast.ai is the best course for anyone with programming experience and you can also use the deeplearning.net Tutorial as a side reference.
If you have a practical experience and would love to understand the theory behinds it then Deep Learning Book is the Bible.

For me the mindset means a lot of different things.

1. Go beyond what people tell you. Discover your own truth.

2. The love for tinkering.

3. The idea that what you bought is your own and you can do anything with it.

4. To use something in a way that is completely not how it is intended.

5. Deep reverse engineering dives.

6. The guilty pleasure of picking digital locks.

I think I've books in all those directions.

Coursera Machine Learning is one of the best places to start, but there are countless resources. There's a huge, wonderful open list of links at Github[1]. I definitely recommend you take a look at it

There are also another great resources online, like those I list below:

1.) In-depth introduction to machine learning in 15 hours of expert videos[2]

2.) Deep Learning Tutorial (@ ufldl.Stanford.edu/tutorial/, can't post the link because I'm out of mana, I mean, not enough reputation yet)

[1]: https://github.com/josephmisiti/awesome-machine-learning

[2]: https://www.dataschool.io/15-hours-of-expert-machine-learnin...

Sounds amazing! Just to add a usecase - for many people, creating a decent voiceover is one of the big sticking points for producing youtube videos or educational courses. If I could write a script, and have software generate a decent enough voiceover, it would be amazing.

It's not even necessary to copy anyone's voice, as long as there's a selection of the most comprehensible and human-sounding ones.

Then, you could even automatically generate slideshow presentation from a few illustrations and headlines, and that would make "rendering" articles into videos very fast and easy. I'm sure a lot of people would pay for such service.

----

By the way, recently I've encountered Deep Voice 2, a similar research project by baidu:

http://research.baidu.com/deep-voice-2-multi-speaker-neural-...

Results are very impressive.

I've responded to this sort if inquiry before, so forgive the copy-pasta:

>> You sound like you have anxiety problems. What have you done to address your anti-social tendencies? Are you going to a therapist? Do you expect a fairy to fly into your house and magic them away? What job do you think exists where you don't need these skills?

>> Having a therapist does not mean you are crazy, and you don't NEED to be crazy to have one. It means you have having a neutral person who helps you track and set goals, track your moods, and help you process work relationships and events. Michael Jordan has a coach, brain workers have therapists. ( https://news.ycombinator.com/item?id=18277170 )

One thing I want to make clear is that this is not going to go away without actual effort and planning on your part.

I would recommend going to a therapist and having them help you process your social interactions and set goals for improving yourself. Which, overall, is what a therapist does. Way more than the cliche "Now let's talk about your father..."

A lot of good information in here, as well. Read some books, it's good for you! It makes you smarter! People have taken time to write them for the last thousand years for a reason!

You can spare the time away from social media to read a book, I promise. And the sense of achievement you get from finishing a book feels great.

- "How to Win Friends and Influence People" is a must-read.

- "Getting to Yes" is another excellent book about workplace conflict resolution.

- There are a ton of books about emotional intelligence. Find one that sounds interesting to you and read it.

I'll also recommend "Deep Work" and "Smarter, Faster, Better" for more general workplace productivity management, but feel free to sleep on those if you feel like it.

This article mourns the loss of something that only has ever existed among a very small minority of any population in recorded history. Deep Literacy, as the author defines it, relies on the luxury of time that most individuals around the world do not have. It is the great disconnect between academia and the rest of society. This article took a very large amount of my time to read in proportion and relation to the point it was trying to make. It could have easily been 500-1000 words, but it was written in the scholarly tradition of more is more. It did not value the readers time.

Are there more distractions now? Certainly, but it is an elitist view to act as if this impacts the amount of deep reading your average citizen of any given country partakes in. If anything, the proliferation of smart phones will increase base level literacy among the masses globally allowing more individuals from more diverse backgrounds the opportunity to one day have the opportunity to engage in deep literacy.

What we are seeing now is more people engaging in conversations and debates that were previously relegated to the elites—academics, politicians, aristocrats. Are their opinions impacted by their lack of deep literacy? Certainly, but their opinions always have been, their voices just weren’t heard quite so loudly before.

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Because Deep Work is more a "how", while The Shallows is more of a "why". It's good to know why, before you try to get to know how. Specifically, The Shallows is an investigation into what the Internet and modern technology does to the brain and what kind of side-effects it may have (affecting our ability to focus). It's a very well-written and accessible book.

Deep Work is more of a how-to book - about how to get ahead in world that's losing its ability to focus (due to the stuff described in The Shallows).

Plesently surprised w/ the choice of algorithms AWS provides. They skip classic MF and go straight to the good stuff:

* Item-Item CF (workhorse amazon original from 2003 w/ modern enhancements)
* Deep Pooling Models (à la Covington. YouTube Deep Rec.)

Although I'm a bit perplexed as to the difference between Deep-FM Recipe
and the FFNN Recipe as it describes the same thing...

https://docs.aws.amazon.com/personalize/latest/dg/working-wi...

Surprised (and delighted!) to see this pop up on the front page of HN today! This course is designed to teach proficient coders the practice of deep learning, that is: how to train accurate models; how to test and debug models; and key deep learning concepts. It covers applications in vision, natural language processing, tabular data, and recommendation systems.

If you're interested in diving deeper into the papers and math behind the scenes, as well as coding from the lowest levels (right down to the compiler level), you'll be interested in "Deep Learning from the Foundations", which is coming out in 2 weeks. The last two lessons are co-taught with Chris Lattner (creator of Swift, LLVM, and Clang).

If you want to understand the underlying linear algebra implementation details, have a look at "Computational Linear Algebra for Coders": https://github.com/fastai/numerical-linear-algebra

If you want to learn about decision trees, random forests, linear regression, validation sets, etc, try "Introduction to Machine Learning for Coders": https://course18.fast.ai/ml

(All are free and have no ads. They are provided as a service to the community.)

Let me know if you have any questions!

Thanks for the details. I've now added a new entry to my list of places never to go. I do hope those little guys were able to resettle somewhere less prone to anthropogenic climate change ;)

Places to Avoid, list written + maintained by Arthur Collé:

[] Deep space [added: after seeing Gravity]

[] Mariana Trench [added: after seeing Underwater]

[] Amazon rainforest [NEW, added: 2021-01-20]

> From Lord British's Treatise on "What is an Ultimate RPG?":

> * Fully interactive virtual world - If it looks usable, it should do something
> * Deep original fiction - Ethical parables, cultural histories, fully developed alternate language text

This reminds me of the first Deus Ex. Wonder if they'll be able to pull it off.

You can e.g. do this using semantic parsing, I remember some older papers but cannot find them right now. For a more recent paper see [1] (haven't read it though).
You can also use Deep Learning for this. There are some interesting approaches using Reinforcement Learning [2]

[1] https://www.aclweb.org/anthology/P18-1034
[2] https://arxiv.org/pdf/1709.00103.pdf

It does lead to intelligence and knowledge of the world. It leads to somewhat similar understanding of the world as our understanding of 4D, quantum mechanics or environment close to a black hole. Pretty bad one, mostly based on repeating whatever we have read about in popular science literature without any intuitive understanding. GPT-3 understanding of physical world is similarly bad. It's just simply a modality not available to the model.

But GPT-3 is able to do few-shots learning. If you show it couple of times how to do something it will try to repeat after you showing some rudimentary understanding of what you are trying to do. It's even able to learn to do simple analogies [0].

GPT-3 is able to do very, very simple arthritics like addition and subtractions with 2-3 numbers. Actually even a lot smaller models trained on arthritics are able to do impressive feats like symbolic integration and solving differential equations.

> Deep Learning for Symbolic Mathematics [1]: Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations. We propose a syntax for representing mathematical problems, and methods for generating large datasets that can be used to train sequence-to-sequence models. We achieve results that outperform commercial Computer Algebra Systems such as Matlab or Mathematica.

What is true is that GPT-3 is not very good at following instructions. It would not be able to execute an algorithm. So, it would not learn arithmetic from instructions if you would not include explicit examples.

[0] https://medium.com/@melaniemitchell.me/can-gpt-3-make-analog...
[1] https://arxiv.org/abs/1912.01412

Some more info on how the old line that "saturated fat is bad" might not be so simple.

https://examine.com/nutrition/is-saturated-fat-bad-for-me/

You can even find people who are into various forms "traditional foods" (not diet trends like paleo...though maybe that too?) go further than more cautious articles like this in affirming -benefits- of saturated fat. Catherine Shanahan for example argues in Deep Nutrition that saturated fat can be good.

RecSys 2016

Covington, Paul, Jay Adams, and Emre Sargin. "Deep neural networks for youtube recommendations." Proceedings of the 10th ACM conference on recommender systems. 2016.

Nvidia furthermore implemented/released it for TensorFlow, and for their new Recsys engine:

https://ngc.nvidia.com/catalog/resources/nvidia:wideanddeep_...

1. Deep Learning. No immediate use for it, but I feel it's an important skill to know.

2. Powershell (on Linux.) I'm already proficient with the traditional shells, but I want to see if this is overall a better CLI and/or scripting environment than, say, Fish. (This was prompted by the recent post about a similar thing newly implemented in Rust.)

3. APL, getting back on it after a long time and potentially writing my own compiler and dialect, if I feel inspired.

4. Consolidating Japanese, mostly the spoken language, because I don't have the time nor will to memorize 1000s of characters. I'm already intermediate level, so at this point I mainly watch movies / shows every day, hoping some of the vocab will stick to my mind.

I post this now (the project is a few months old) purely inspired by this post: https://blog.photoeditorsdk.com/deep-learning-for-photo-edit...

They basically took the same idea and instead of just producing a hack finished a nice full version of the idea - very nice write up!

But, they don't have any web UI for it yet, so I am getting tempted to revive my dormant side project and likewise make a proper polished version of it with a web interface. If anyone on here is a talented web dev with an interest on working on this as a side project (for free, and for fun, though we could explore monetization if it works), feel free to get in touch! (ps extra get in touch if you are near south bay area/Stanford)

PS Incidentally, I independently came up with the same idea as in Deep Interactive Object Segmentation (https://arxiv.org/abs/1603.04042) and implemented it for my Stanford CS 229 (Machine Learning) project first - the hack came later. The ObjectCropBot hack allows only cropping and not clicking because it was faster to hack in that, but I think it is ideal to allow both cropping to constrict around target object and clicking (clicking alone leaves too much ambiguity , eg do you want to crop the person, or their shirt, or a subset of that shirt).

I would highly recommend going through François chollet's Deep Learning with Python[1] book. The technical concepts are explained very well and since you have gone through the Modern AI book you won't have an issue understanding them. It's a very hands-on book and by the time you finish it you will be able to use Deep Neural Nets to solve many problems.

I would also recommend going through the scikit-learn documentation. Some of the tutorials/examples there are pretty good.

At the end of the day, it all comes down to your personal learning style. For me the thing that worked was to go through the above mentioned steps and then find a problem I was interested in and try to solve it using my newly found skills. That way you will discover new tools and methods.

Finally, the Deep Learning [2] book is also very good but I would not recommend it to a beginner. It's better to use it when you have a basic understanding of Machine Learning and you want to gain a deeper understanding of the concepts.

[1]:https://www.manning.com/books/deep-learning-with-python

[2]: https://www.deeplearningbook.org/

In addition to many of the games listed here, also consider:

* Deep Sea Adventure — A tiny push-your-luck game that blends theme and mechanics perfectly, is easy to explain, and is a ton of fun without being too thinky. It also scales perfectly from 2 to 6 people. Expensive if you're just counting components, but the value of the game and the fun it brings makes it worthwhile.

* Celestia — Also push-your-luck style, but with a bit more production (and, oddly, cheaper than Deep Sea Adventure).

* Patchwork — A great little two-player game where you're making a quilt. That sounds silly, but it's like Tetris that's powering a small economical engine. Really fun.

* For Sale — Good for 3-6 players, a really simple little card game where you get properties in the first round and auction them off in the second. Plays in 15 minutes, fits in a small box. (I think they even make a travel version.)

* Santorini — the retail version is a big production, but if you get it, you could easily make a smaller version of it. Takes 30 seconds to learn and is hard to really master. Comes with a ton of God power cards that make a bunch of replayability.

* Smash Up — the base set comes with 8 factions. Each player takes two factions (of their choice, at random, etc.) and smashed them into a deck. You then compete against others to topple bases and score points. A lot of fun with the right crowd. I think they've released about 50 factions total so far, with 8 new ones released each year currently. All totally optional.

* Star Realms — great little two-player deck builder. It's space themed, if you want another theme, they have other versions that work similarly. It's just one deck of cards, plus expansions if you like it.

* Dixit — I love Dixit. I've never played it with anyone that didn't have fun. The box really spreads things out, and is mostly holding the scoring track, but you could replace that with something smaller easily. A deck of amazing art cards, a couple of voting tokens, and a way to keep score. Could fit into a sandwich bag if you create a smaller scoring system.

Deep Thought Tabs for Firefox adds funny, profound observations in newly opened empty tabs, with fresh images each time.

* Few interesting bits of code:

   - Fisher-Yates shuffle algorithm.
   - Ken-Burns effect.
   - Visual (image-preview) tooltips in CSS+JS.
   - Asynchronous XMLHttpRequest.

* Checkout the source at https://github.com/TheCodeArtist/deep-thought-tabs/tree/mast...
to learn creating webextension addons for Firefox in pure JavaScript (no external libs).

With all this talk of the colossal ITER project merely being a warm-up for the even bigger DEMO one to follow which, in its turn will be but a pre-cursor to the real thing, I can't help being reminded of Douglas Adams's 'Deep Thought'

“There is no problem,” said Deep Thought with magnificent ringing tones. “I am simply the second greatest computer in the Universe of Space and Time.”

“But the second?” insisted Lunkwill. “Why do you keep saying the second? You’re surely not thinking of the Multicorticoid Perspicutron Titan Muller are you? Or the Pondermatic? Or the . . .”

Contemptuous lights flashed across the computer’s console.

“I spare not a single unit of thought on these cybernetic simpletons!” he boomed. “I speak of none but the computer that is to come after me!”

Fook was losing patience. He pushed his notebook aside and muttered, “I think this is getting needlessly messianic.”

“You know nothing of future time,” pronounced Deep Thought, “and yet in my teeming circuitry I can navigate the infinite delta streams of future probability and see that there must one day come a computer whose merest operational parameters I am not worthy to calculate, but which it will be my fate eventually to design.”

Thank you so much for this, for me Deep learning Part 1 was a top notch course that really helped me learn by actually doing things in variety of topics (e.g competing in Kaggle, creating spreadsheets to understand collaborative filtering & embeddings, sentiment analysis using CNN and RNN etc).
I found the top down approach to very effective in keeping me motivated as I worked my way through the course.
It took me 6 months of watching(and rewatching) the videos and working on problems to get comfortable.

I have done a few MOOCs: Andrew Ng's machine learning, Coursera ML specialisation, edx Analytics Edge and all of them were good learning experience but fast ai's deep learning part 1 really stood out.

For me, the combination of Deep Learning Book + Fast ai MOOC + CS231n (youtube videos & assignments) cover almost everything I want to learn about the subject.

@jph00, I'm half way through neural style transfer and I am loving it.

> Even a huge believer in technology like me has to laugh when some character talks about how they’re going to change the world with an app that tells you whether what you’re eating is a hot dog or not.

sorry bill but one could argue in a round about way that andrej karpathy did do just that ;P

the hot dog identifying app is one of my favourite examples of how spot on the show is

here(o) is a question i asked to one of the show's technical consultants whether the choice of 'not hotdog' was a reference to one of karpathy's early demos(i||ii)

timanglade> Ha seems like a fun coincidence.

(o) https://news.ycombinator.com/item?id=14639161 the yt link is now a dead link.. use either of the below

(i) https://www.youtube.com/watch?v=u6aEYuemt0M&t=465 ; Title: Deep Learning for Computer Vision (Andrej Karpathy, OpenAI) ; Desc: 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).

(ii) https://www.youtube.com/watch?v=eyovmAtoUx0&t=5787 ; timestamped from the full stream; Title: Bay Area Deep Learning School Day 1 at CEMEX auditorium, Stanford ; Desc: Day 1 of Bay Area Deep Learning School featuring speakers Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov and Andrew Ng. ;

I totally agree with you.

I started Deep Learning Specialization in Coursera: https://www.coursera.org/specializations/deep-learning last month and almost finished it, but I realized this field requires a lot of expertise, not something you can learn in a month. What I learned in the courses was just a basic topics in Deep Learning and how to use Numpy, TensorFlow and Keras.

I was considering diving into a Data Science job and started that specialization as a starting point, but I just realized how foolish I am. Chances are I'll find a job, but it definitely takes another 10 years/10,000 hours to master this discipline.

Anyway, the specialization is wonderful and Dr.Ng explains complicated Deep learning topics in a way that is understandable for everyone. So if just learning is what you want, you should take it, but I don't think you are prepared for a real world Data Science job after finishing it.

A book that explains the core ideas of neural networks and deep learning. Cool because:

* The book incorporates lots of running code for readers to explore and extend.

* The book's philosophy is to go deep into the core concepts of deep learning, not to superficially cover a long laundry list of ideas. This gives readers a solid foundation to build on, and makes understanding other material much easier.

* Deep learning is the most powerful approach known to many problems in image recognition, speech recognition, and natural language. The book will help lots more people get quickly up to speed.

The book will be freely available online, and a beta site is coming soon. Pre-beta mailing list here: http://eepurl.com/BYr9L

LinkedIn, Mountain View, CA. Local only.
Our search back-end team is growing, and we are looking for an experienced manager for it. Job description pasted below.

Email dbasch AND iperisic at linkedin dot com

Responsibilities:

· You will lead an innovative team of software developers to design, build and maintain our search infrastructure.
· You'll lead through uncertainty and collaborate with other engineering teams, Business Development, Legal, and teams across the organization to make things happen to achieve desired results.

Requirements:

· Deep Software Technical Skills: You are deeply technical, understand how to break down problems and design extensible solutions. You have ample development experience in Java (preferably also C++ or other JVM-based languages such as Scala), as well as scripting languages. You know how to scale systems to a billion calls per day, how to parallelize requests, and how to build infrastructure and APIs for softwareservices. You have experience building large-scale information retrieval solutions, and understand search engines inside and out. You are intimately familiar with concepts such as garbage collection algorithms, parsing, lexing, building inverted indexes, ranking algorithms, etc. You have deep knowledge of the fundamental concepts discussed in books such as Modern Information Retrieval or Managing Gigabytes.
· You likely have a BS, MS or PhD in Computer Science or closely related field.

· Management and Cross-Organizational Influence: You have demonstrated successful leadership in building and leading small, high-performance engineering teams. You understand the value of relationships and are highly effective at influencing cross-organizational teams to implement internal component APIs to meet the needs of external consumers. You communicate effectively to all levels inside the company and with partners.
· You're a very hands-on technical manager who can influence and lead the architecture, design and development of a scalable, high performance, and high reliability platform.

This talk is an introduction to Deep Learning with PyTorch Lightning. We will briefly overview of Deep Learning fundamentals, and then learn how to easily implement various deep learning tasks, including Computer Vision and Natural Language Processing, all while leveraging the scalability and flexibility of PyTorch Lightning.

Ari Bornstein is Head of Developer Advocacy at Grid.ai and PyTorch Lightning. Previously, Ari scaled Microsoft's AI/ML global advocacy efforts as an Open Source Engineer.

Ari's Computer Science Masters Research at Bar-Ilan University concentrated on AI and NLP with an emphasis on multi-document semantic representation, summarization and coreference resolution. He also holds dual degrees in History and Computer Science from Goucher College.

Follow Ari:
@pythiccoder
https://www.linkedin.com/in/aaron-ari-bornstein-22aa7a77/

Check out the page for the "International Conference on Bayesian Statistics for Machine Learning":

https://waset.org/bayesian-statistics-for-machine-learning-c...

What struck me was a section called "Special Journal Issues":

Special Journal Issues

ICBSML 2020 has teamed up with the Special Journal Issue on Bayesian Statistics for Machine Learning. A number of selected high-impact full text papers will also be considered for the special journal issues. All submitted papers will have the opportunity to be considered for this Special Journal Issue.

Of course there is no such thing as a "Special Journal Issue". There are special issues of particular journals, for example the Special Issue of the Machine Learning Journal on Learning and Reasoning (that I was looking for when I stumbled on this page).

Every single conference on the site has the same text in this section... and in every other section with minor variations.

There are more hints:

1) The names of the conferences seem to have been generated by prepending "International Conference" in front of legit conference names.

2) The "Call for papers" page is just a list of subject fields.

3) The "Selected papers" don't have anything to do with the actual conference subjects and are in fact scraped from other conference and journal sites. For example, in the "ICBSML" page there's a paper titled :

Deep Learning Based Fall Detection Using Simplified Human Posture

But if you click the link you see a pdf with this rubric on top:

  World Academy of Science, Engineering and Technology 
  International Journal of Computer and Systems Engineering 
  Vol:13, No:5, 2019

4) etc etc.

It's a scam.

When I was at school one of my professors joked that the halting problem would ensure that, whatever else we were able to automate, there would always be jobs for programmers. I wonder.

A while ago there was the automatic statistician [1,2] which can do various statistical analyses and reporting automatically. This year there was a paper out of MIT on Deep Feature Synthesis, in which a largely automated system did quite well on Kaggle problems [3]. Now this, which seems like it could produce solutions to some problems I've used in technical phone screens.

At some point, someone will write a framework which automates the process of finding human cognitive tasks to automate, and someone else will give write a cost function of automated-task-to-business-need-mismatch which is amenable to optimization, and then we can all go home.

[1] http://www.automaticstatistician.com/index/
[2] http://mlg.eng.cam.ac.uk/lloyd/talks/jrl-auto-stat-msr-2014....
[3] https://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/uplo...

Deep Learning: multi-level representational learning that works.

Here is good authoritative definition:

"Deep-learning methods are representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level." from Deep Learning, Nature Volume 521 issue 7553, 2015, LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey

History: Multi layer neural networks, convolution networks, recurrent networks etc. are old techniques that have existed for decades. They used to be very slow and using them seemed to be dead end theoretically.Canadian Mafia (Geoffrey Hinton, Yann LeCun and Yoshua Bengio) from University of Montreal worked diligently and solved many theoretical and practical problems and made these algorithms usable in practice. GPGPU:s hastened this process.

This seems like a sincere comment, so in case it's helpful, here's my interpretation of why others may be downvoting it.

Deep learning practitioners are aware that they're using non-convex optimization, stochastic gradient descent, and so on. In fact, those topics are core to modern deep learning research and explicitly acknowledged in most published research: LSTMs were invented to solve the vanishing gradient problem, Restricted Boltzmann Machines were used as a pre-training step to avoid local minima, and optimizers like ADAM have explicit guarantees about things like convergence.

You may know all this stuff already--not sure, based on your comment above.

If not, here are a few example papers from mainstream AI researchers explicitly talking about deep learning as an optimization problem or function approximator:
[1] Why Does Unsupervised Pre-training Help Deep Learning?http://www.jmlr.org/papers/volume11/erhan10a/erhan10a.pdf
[2] Multilayer feedforward networks are universal approximators: http://www.sciencedirect.com/science/article/pii/08936080899...
[3] Deep Learning, Nature. http://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf
[4] Adam: A Method for Stochastic Optimization. https://arxiv.org/abs/1412.6980

I agree. Although one could imagine that concepts conveyed in a photograph could be extracted and abstracted as vectors -- just like word2vec and its successors. Of course, there is a long way to go before we hit "human understanding" parity, but I think ideas from [1], [2] and [3] could be extrapolated in doing just that.

[1] Deep Visual-Semantic Alignments for Generating Image Descriptions - cs.stanford.edu/people/karpathy/cvpr2015.pdf

[2] Deep Learning for Content-Based Image Retrieval - www.research.larc.smu.edu.sg/mlg/papers/MM14-fp336-hoi.pdf

[3] Deep Learning for Content-Based Image Retrieval - www.cs.rutgers.edu/~elgammal/pub/MTA_2014_Saleh.pdf

I use NVidia-Docker extensively in my Open Source project Deep Video Analytics [1] when combined with TensorFlow (which allows explicit GPU memory allocation) its unbeatable in running multiple inference models on a single GPU in a reliable manner. Combining this setup with docker volumes on AWS EFS allows simple multi machine deployments.

[1] https://github.com/AKSHAYUBHAT/DeepVideoAnalytics

Can someone explain a bit about the problems? I am not a Neuroscientist but a Machine Learning researcher, and given the recent results in Deep Learning / Artificial Neural Networks which reach Human performance in certain specific tasks, it almost looks like if you would just put an ANN together which is somewhat big enough and somewhat similar wired together as the Human brain, you would yield something similar. And the point is, it doesn't really matter at all whether that is exactly like in the Human brain, and also, you don't really need to understand in detail how it will work, like you can even not really explain the current simple ANNs. It just works anyway. So, under this view, it's just a matter of scaling up, and to wait until we have enough computing power. And then, you would add more details to make it more close to the real Human brain.

1. Get rid of my lower back pain- I’ve had back pain and for as long as I can remember. Poor sitting posture, heavy backpacks, and sleeping curled up probably contributed to it. I tried an ergo chair (no results), a medicine ball (less pressure on my lower back, but I slouch horribly), and a standing desk (much better on my back, but caused knee pain). Stretching helps, but inflames previous injuries. I suspect a lot of stress in my life comes from repeated failed attempts to alleviate these simple pains.

2. Deep Breathing - I’m trying to meditate, but my mind wanders after a minute or two. Trying to fight off the countless notifications, chrome tabs, noises, etc that have shortened my attention span. Goal is to hit 10 minutes of focusing on nothing.