Basically, each connection gets an arrow. It also supports self-connections and gates.
There are some examples of images:
Play around yourself here.
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 ...
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 ...
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 ...
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 ...
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 (...
However, do industrial strength, production ready defensive strategies and approaches exist? Are there known examples of applied adversarial-resistant networks for one or more specific types (e.g. for small perturbation limits)?
I think it's difficult to tell whether or not there are any industrial strength defenses out there (which I assume would mean that ...
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
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).
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
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.
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.
XPU is a device abstraction for Intel heterogeneous computation architectures, which can be mapped to CPU, GPU, FPGA and other accelerators. The "X" from XPU is just like a variable, like in maths, so you can do X=C and you get CPU accceleration, or X=G and you get GPU acceleration... That's the intuition behind that abstract name.
In order to ...
Another good resource is the free CatalyzeX browser extension — it adds in-line links to any relevant code wherever you come across papers on various websites: AI/ML Papers with Code Everywhere - CatalyzeX
The corresponding website is catalyzeX.com.
Full disclosure: I'm one of the creators. It's actively maintained and ...
From the theoretical foundations one can look into the Chapter 20: Deep Generative Models of the classic DL book by Goodfellow, Bengio https://amzn.to/2MmZNbH. Not the most recent reference, but written by the professionals in simple and accessible way.
There is a nice book Generative Deep Learning by D.Foster with some simple heuristics and probability ...
For genetic algorithms, I have written GeneticSharp.
A multi-platform genetic algorithm library for .NET Core and .NET Framework. The library has several implementations of GA operators, like: selection, crossover, mutation, reinsertion and termination.
Fann (http://leenissen.dk/fann/wp/) is a free open source neural network library.
Multilayer Artificial Neural Network Library in C
Backpropagation training (RPROP, Quickprop, Batch, Incremental)
Evolving topology training which dynamically builds and trains the ANN (Cascade2)
Easy to use (create, train and run an ANN with just three ...
There is also DXNN, which is as you described, a neuroevolutionary system, it is written in Erlang.
I did some work on it to make it modular, so you use it as a library and keep your code/application isolated.
Here is a code example, which downloads DXNN as a library. It also generates Gnuplot ready data files for ...
Well, if you choose TensorfFlow to work with, you get TensorBoard as part of the package. That might be something close to what you're looking for.
And with TensorFlow, you can code in C++, Python, and a few other languages (I think there are both Ruby and Java bindings as well, probably others by now).
You might have come across the Tensorflow Playground which has a wonderful visualization of the network connections and the neuron weights.
There are so many different versions of spiking neural networks out there. I think it is mainly due to the fact that there has been no dominant successful SNN model with proper learning algorithm like CNN with BP. However, there have been several recent papers(e.g. SuperSpike, SLAYER) on SNN that may lead to the standard framework for SNN. It happened within ...
There is a book Spiking Neuron Models: Single Neurons, Populations, Plasticity (2002) by Wulfram Gerstner and Werner M. Kistler.
I also found these slides that could be useful.
(Additional resources would be appreciated.)
I don't know about voice recognition, but, for NLP, I think that Gensim could be what you are looking for.
Gensim is an NLP package that contains efficient implementations of many well-known functionalities for the tasks of topic modeling, such as tf–idf, Latent Dirichlet allocation, Latent semantic analysis, etc.