You need to get on board the PyTorch bandwagon now to harness its full potential and give your deep learning career a major boost.
Open Source Artificial Intelligence (AI), NLP and Other Data Science Projects Plato – Tencent’s Graph Computing Framework Graphs have become an important part of the machine learning lifecycle in recent times.
They are an effective and efficient method of analyzing data, building recommendation systems, mining social networks, etc.
In short – they are super useful.
In fact, we at Analytics Vidhya are big proponents of graphs and have a collection of useful articles you can read about here.
Plato is a framework for distributed graph computation and machine learning.
It has been developed by the folks at Tencent and open-sourced recently.
Plato is a state-of-the-art framework that comes with incredible computing power.
While analyzing billions of nodes, Plato can reduce the computing time from days to minutes (that’s the power of graphs!).
So, instead of relying on several hundred servers, Plato can finish its tasks on as little as ten servers.
Tencent is using Plato on the WeChat platform as well (for all you text savvy readers).
Here’s a comparison of Plato against Spark GraphX on the PageRank and LPA benchmarks: You can read more Plato here.
If you’re new to graphs and are wondering how they tie into data science, here’s an excellent article to help you out: Let’s Think in Graphs: Introduction to Graph Theory and its Applications using Python Transformers v2.
2 – with 4 New NLP Models!.HuggingFace is the most active research group I’ve seen in the NLP space.
They seem to come up with new releases and frameworks mere hours after the official developers announce them – it’s incredible.
I would highly recommend following HuggingFace on Twitter to stay up-to-date with their work.
Their latest release is Transformers v2.
0 that includes four new NLP models (among other new features): ALBERT (PyTorch and TensorFlow): A Lite version of BERT CamamBERT (PyTorch): A French Language Model GPT2-XL (PyTorch and TensorFlow): A GPT-2 iteration by OpenAI DistilRoberta (PyTorch and TensorFlow) As always, we have the tutorials for the latest state-of-the-art NLP frameworks: How do Transformers Work in NLP?.Demystifying BERT: A Comprehensive Guide to the Ground-Breaking NLP Framework OpenAI’s GPT-2: A Simple Guide to Build the World’s Most Advanced Text Generator in Python ARC – Abstraction and Reasoning Corpus (AI Benchmark) This is a slightly different project from what I typically include in these articles.
But I feel it’s an important one given how far away we still are from even getting close to artificial general intelligence.
ARC, short for Abstraction and Reasoning Corpus, is an artificial general intelligence benchmark that aims to emulate a “human-like form of general fluid intelligence”.
This idea and the research behind it has been done by François Chollet, the author of the popular Keras framework.
Chollet, in his research paper titled “On the Measure of Intelligence“, provides an updated definition of intelligence based on Algorithmic Information Theory.
He also proposes a new set of guidelines to showcase what a general AI benchmark should be.
And the ARC is that benchmark based on these guidelines.
I think its a really important topic that will spur a lot of debate in the community.
That’s a healthy thing and will hopefully lead to even more research on the topic and perhaps a big step forward in the artificial general intelligence space.
This GitHub repository contains the ARC dataset along with a browser-based interface to try solving the tasks manually.
I’ve mentioned a couple of resources below to help you understand what AI is and how it works: Artificial Intelligence Demystified Certified AI and ML Blackbelt Program End Notes So, which open source project did you find the most relevant?.I have tried to diversify the topics and domains as much as possible to help you expand your horizons.
I have seen our community embrace the deep learning projects with the enthusiasm of a truly passionate learner – and I hope this month’s collection will help you out further.
Personally, I will be digging deeper into François Chollet’s paper on measuring intelligence as that has really caught my eye.
It’s rare that we get to openly read about benchmarking artificial general intelligence systems, right?.I would love to hear from you – let me know your ideas, thoughts, and feedback in the comments section below.
Also, just wanted to reiterate the key links I have mentioned throughout the article: Applied Machine Learning – Beginner to Professional Natural Language Processing (NLP) using Python Computer Vision using Deep Learning 2.
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