Overview
- Presenting 11 data science videos that will enhance and expand your current skillset
- We have categorized these videos into three fields – Natural Language Processing (NLP), Generative Models, and Reinforcement Learning
- Learn how the concepts in these videos work and build your own data science project!
Introduction
I love learning and understanding data science concepts through videos. I simply do not have the time to pour through books and pages of text to understand different ideas and topics. Instead, I get a much better overview of concepts via videos and then pick and choose the topics I want to learn more about.
The sheer quality and diversity of topics available on platforms like YouTube never ceases to amaze. I recently learned about the amazing XLNet framework for NLP from a video (which I have mentioned below for your consumption). This helped me grasp the concept so I could explore more about XLNet!
I strongly believe structure is very necessary when we’re learning any concept or topic. I follow that approach each time I write an article as well. That’s why I’ve categorized these videos into their respective domains, primarily Natural Language Processing (NLP), Generative Models and Reinforcement Learning.
So are you ready to dive in and explore the length and breadth of data science through these fascinating videos?
Without any further ado, here are 11 awesome Data Science Videos:
- Natural Language Processing (NLP)
- XLNet explained
- How does Google Duplex work?
- Google’s POEMPORTRAITS: Combining Art and AI
- Generative Models
- Dive into Variational Autoencoders!
- Create Facial Animation from Audio
-
MuseNet Learned to Compose Mozart, Bon Jovi, and More
- Reinforcement Learning
- Teaching the Computer to drive
- Learn how AlphaGo Zero works
- Google DeepMind AI learns to walk
- AI learns to play 2048
- BONUS
- Adobe develops AI to detect Photoshopped Faces
Natural Language Processing (NLP)
XLNet Explained
XLNet is the hottest framework in NLP right now. You simply must be aware of what it is and how it works if you want to carve out a career in this field. I came across this video recently and wanted to share it with the community as soon as possible.
XLNet is the latest state-of-the-art NLP framework. It has outperformed Google’s BERT on 20 NLP tasks and achieved state-of-the-art results on 18 of them. That is very, very impressive.
Make sure you check out our article covering XLNet and it’s powerful ability here.
The below video provides a clear explanation of the original XLNet research paper. Note: You might need to know a few NLP concepts beforehand to truly grasp the inner workings of XLNet.
How does Google Duplex work?
Remember when Sundar Pichai went on stage and sent the whole world into a frenzy when he unveiled Google Duplex in his keynote at Google I/O 2018? I remember listening in complete awe to the super-realistic calls that the AI made.
It took a bit of time for the data science and NLP community to come up with an explanation as to how Google Duplex actually works. It’s pretty powerful and has the potential to change how we interact with machines.
So the million dollar question – did Google Duplex pass the Turing Test!? You decide after watching this video:
Google’s POEMPORTRAITS: Combining Art and AI
I am an artist and the prospect of combining any art form with Artificial Intelligence is extremely enticing. In a world where there is so much fear around AI, such applications are more than welcome.
Google’s POEMPORTRAITS AI has been trained on nineteenth-century poetry using NLP techniques. You can contribute and donate a word to generate your own POEMPORTRAIT. Check out how this awesome concept works:
Generative Models
Dive into Variational Autoencoders!
Here’s one of our favorite reinforcement learning experts Xander Streenbrugge from his wonderful ArxivInsights channel.
Variational Autoencoders (VAEs) are powerful generative models with diverse applications. You can generate human faces or synthesize your own music or use VAEs for removing noise from images.
I like this video a lot. Xander begins with an introduction to basic autoencoders and then goes into VAEs and disentangled beta-VAEs. Quite technical, but explained beautifully and concisely, in typical Xander style.
Xander is coming back to DataHack Summit this year so you can hear from him and meet him in person!
Create Facial Animation from Audio
I was immediately drawn to the video when I read the title. This is Generative Models at their best! You can not only generate facial animation from audio but also generate different emotions for the same audio. And the facial expressions look incredibly natural.
If you aren’t following Two Minute Papers, you’re missing out. They regularly churn out videos breaking down the latest developments in easy-to-understand fashion. It’s a gem of a channel.
MuseNet Learned to Compose Mozart, Bon Jovi, and More
Another entry from the Two Minute Papers archive.
OpenAI’s MuseNet is a deep neural network that generates musical compositions with different instruments and combines different styles. It uses the same general-purpose unsupervised technology as GPT-2 and the results are amazing.
Never heard of GPT-2? It’s an NLP framework on par with XLNet. Check out how MustNet works here:
Reinforcement Learning
Teaching a Computer to Drive
Self-driving cars have always fascinated me. The sheer scale of an autonomous vehicle’s project is staggering. There are so many components, both on the hardware side as well as the data science side, that need to align for this project to work.
This is a perfect video for beginners to learn about Genetic Programming and Reinforcement Learning and how they are used to create powerful applications. Simon’s personality kept me hooked until the very end.
And I am definitely trying the project on my own.
Learn how Google DeepMind’s AlphaGo Zero works
Another great video by Xander. He explains Google DeepMind’s popular paper on AlphaGo Zero.
AlphaGo Zero is a new version of the original AlphaGo program that beat human champion Lee Sedol comprehensively. I recommend reading our article on Monte Carlo Tree Search, the algorithm behind AlphaGo before proceeding to learn about AlphaGo Zero.
AlphaGo Zero uses Reinforcement Learning to beat the world’s leading Go players without using data from human games.
“AlphaGo Zero surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0, reached the level of AlphaGo Master in 21 days, and exceeded all the old versions in 40 days.”
Source: Wikipedia
Google DeepMind’s AI learns to Walk
This video is both hilarious and informative. Exactly the type of video I like when I’m learning new things! It was funny to watch the AI learn to walk. But at the same time, it left me marveling at the power of Reinforcement Learning.
The video discusses 3 papers to try and explain how the AI learned to walk and it is surprisingly simple to understand.
AI learns to play 2048
Have you ever played the 2048 game? It is super addictive once you get the hang of it. I used to easily finish games earlier but not anymore. Being a data science enthusiast, I am going to train my computer to play it with the help of this awesome video.
This is another example of the use of Genetic Programming and Evolutionary Algorithms.
BONUS: Adobe develops AI to detect Photoshopped Faces
Adobe is a market leader in image and video manipulation software. Other companies have tried, but not many have even gotten close to Adobe’s level.
Last month, Adobe announced its research efforts to detect manipulated images. High time someone did that! It will soon be impossible to tell real from fake given how quickly GANs have taken over the world.
Imagine Donald Trump challenging Kim Jong Un to a nuclear war and then claiming that it was a deepfake and shrugging off all responsibility! We need to avoid those situations turning into reality. This video shows how Adobe’s algorithm works and tried to combat fake images:
End Notes
I love knowing about the latest research in data science, machine learning and AI. But I find it hard to read papers. It takes a lot of time and effort – something not every data science professional has. I am sure many of you struggle with the same. Consuming videos is the ideal way to get an overview of these concepts.
You can then pick and choose where your interests lie and try to spin up a project or blog post on it. Trust me, it’s a wonderful way to learn and ingrain new data science concepts.
What are some of your favorite channels or videos on data science? Let’s discuss in the comments below.
