Although I have only partially read (or not read at all) some of the following resources and some of these resources may not cover more advanced topics than the ones presented in the book you are reading, I think they can still be useful for your purposes, so I will share them with you.
I would also like to note that if you understand the contents of the ...
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 ...
Basically, each connection gets an arrow. It also supports self-connections and gates.
There are some examples of images:
Play around yourself here.
There are various dataset available such as
Pascal VOC dataset: You can perform all your task with these.
size of the dataset is as follows
ADE20K Semantic Segmentation Dataset: you can perform only segmentation here
COCO dataset: This is rich dataset but a size larger then 5 GB so you can try downloading using google colab in your drive and then make ...
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 ...
This answer will point the reader to potentially useful resources, but I can't ensure that the courses are good (because I have never followed them).
Reinforcement Learning in the Open AI Gym (a small course that you can find in the YouTube channel suggested in the other answer) by Phil Tabor
The free course Advanced Deep Learning & Reinforcement ...
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.
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
I'll add a few, though I'm also not sure what exactly would constitute an "academic" podcast. I'm not going to link everything, they should be easy enough to find.
This Week in Machine Learning and AI
For the programming part I suggest this YouTube channel by Phil Tabor (he also has a website: neuralnet.ai. I found his videos really useful while I was attending reinforcement learning classes at the uni. He covers basic algorithms like value iteration and policy iteration and also more advanced like deep q learning, covering all main python libraries (...
Recently arxiv.org added a Code Tab towards the end of paper descriptions. Which contains links to both the official and community code.
I don't know if this is the case for all the papers or not till know. But I'm sure it'll be extended to all the papers in a short while.
Neural Network Design (2nd edition) by Hagan et al. is one resource you could look at. It's a huge tome, weighing in at over 1000 pages in pdf form, but it is freely available (you can also buy a dead-tree version if you really want one).
You might have come across the Tensorflow Playground which has a wonderful visualization of the network connections and the neuron weights.
So here’s a couple quick resources that i could think of. First of all, you could look at this,
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 ...
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.
A Comprehensive Survey on Graph Neural Networks (2019) presents a list of ConvGNN's. All of the following accept weighted graphs, and three accept those with edge weights as well:
And below is a series of open source implementations of many of the above:
I have not found any simple implementation of a naive EBMT system, but I found some articles, papers and books that may be helpful (although I haven't read them, apart from the first and last one), so I will list them below.
The web article Example-based machine translation provides a decent high-level explanation of example-based machine translation.
Reinforcement Learning: An Introduction by Richard Sutton and Andrew Barto is undoubtedly one of the best books, to begin with. Despite its age, the book is still the canonical introduction to reinforcement learning. It does require some patience, but I think it's very approachable and rigorous at the same time!
Arthur Juliani has some interesting Medium articles on reinforcement learning with TensorFlow backed up with code on GitHub.
Part 0 — Q-Learning Agents
Part 1 — Two-Armed Bandit
Part 1.5 — Contextual Bandits
Part 2 — Policy-Based Agents
Part 3 — Model-Based RL
Part 4 — Deep Q-Networks and Beyond
Part 5 — Visualizing an Agent’s Thoughts and Actions
Part 6 — ...
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, ...
You can use Pytorch_Geometric library for your projects. Its supports weighted GCNs. It is a rapidly evolving open-source library with easy to use syntax. It is mentioned in the landing page of Pytorch. It is the most starred Pytorch github repo for geometric deep learning. Creating a GCN model which can process graphs with weights is as simple as:
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 ...
If you like papers, this keeps track of the state of the art (a lot of artificial intelligence, but no exclusively though)
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 powered by ...
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, ...
The brat annotation tool has provided some useful scripts for converting the annotations format including standoff format to CoNLL. Please see this source code from brat GitHub repo for converting the .ann and .txt inputs (standoff format) to .conll file: https://github.com/nlplab/brat/blob/master/tools/anntoconll.py
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I think by random search, you are referring to so-called "black-box optimization". Random search is sometimes used as a name for this, but BBO is a more common name, and might be easier to search for.
There are many BBO techniques. 'random search' is usually used to refer to a hill-climbing algorithm where you start at a random ...