6

For sites, https://towardsdatascience.com/ maybe a good choice. The articles on there is about AI. You can read a lot of articles about state-of-the-art AI networks there. You can also subscribe to their newsletter for emails everyday on new AI discoveries. However, as stated by @DuttaA, take the articles with caution. The articles may or may not be entirely ...


5

If you're willing to "drink from the fire hose", it's probably hard to beat just browsing recent arXiv submissions when it comes to seeing the most novel results in AI/ML. For the unfamiliar, arXiv is a preprint repository where academic articles are published before/as they are submitted to an academic journal. It also contains work which is not intended ...


4

Besides the other answers, you can follow the Batch news of deeplearning.ai: The Batch presents the most important AI events and perspective in a curated, easy-to-read report for engineers and business leaders. Every Wednesday, The Batch highlights a mix of the most practical research papers, industry-shaping applications, and high-impact business news.


4

Personally, for such stuff, I always felt twitter was the best. As long as you follow the right people/pages your feed can be quite informative and quite frequently links to articles or youtube videos which can be helpful are also found. To start off you could check out the DL loop list to start you off with a few popular people in DL on twitter


4

In addition to the books already mentioned, I would like to recommend to you some that helped me understand the basics and guided me through my first AI / CI implementations. Computational Intelligence: An Introduction by Andries P. Engelbrecht It includes the most relevant developments in computational intelligence with good discussions on intelligence ...


2

There is an excellent book on neural networks written by Michael Nielsen. Here, take a look - http://neuralnetworksanddeeplearning.com/


2

So here’s a couple quick resources that i could think of. First of all, you could look at this, https://en.m.wikipedia.org/wiki/List_of_lists_of_lists It has classics such accidents, hospitals in Asia, or even a list of famous resignations. It’s easentially a list of random lists of things. It may not owner cover your requirement for sequences but it’ll ...


2

Firstly, you should be clear about on which subject you want to study. AIMA deals with conventional ai algorithms like path planning, logic etc. Elements of statistical learning is a machine learning book which covers most of the machine learning algorithms you will come across (spare deep learning). digital image processing is an entirely different field ...


2

The best resource for learning TensorFlow 1.9 and earlier is this course by Stanford. Also additional resources for the entire overview of TensorFlow and its comparisons with NumPy has been made in this video. For hands on models check these videos by Sentdex and also some high level tutorials by Hvass Labs.


2

I found the following detailed and well documented Python notebook, which uses only NumPy.


1

Backpropagation is actually a lot easier than it is made out to be - if you have a basic understanding of calculus and the chain rule, and the single multi-variable calculus rule that to combine 2 gradient vectors, you simply add them. This is hands down the best walk through of back prop I've found on the internet. If you are still confused after that, ...


1

The first place I would have directed you would be Sklearn and pydiffmap. I found this paper specifically about the problem you are doing using python the reference a package called megaman It seems like an active Github . I suggest not just looking at manifold learning papers but leaning towards a search toward non linear embedding or non linear ...


1

If you like papers, this keeps track of the state of the art (a lot of artificial intelligence, but no exclusively though) https://paperswithcode.com/sota The mission of Papers With Code is to create a free and open resource with Machine Learning papers, code and evaluation tables. We believe this is best done together with the community and ...


1

I think Deep Learning Weekly https://www.deeplearningweekly.com/ is very good. Critiques of other sources mentioned elsewhere in these answers: towardsdatascience and similar have a lot of articles which seem like undergrads trying to write out their best understanding of their lecture notes. Twitter is pain. The Batch is ok but a lot less interesting, ...


1

I don't know if you are looking for something in a library, but I've found this in a public Github (I've not checked deeply if it fits for you). I hope that's what you're looking for.


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