Data science SQL interview questions from top tech firms - Oct 5, 2021.
As a data scientist, there is one thing you really need to understand and know how to handle: data. With SQL being a foundational technical approach for working with data, it should not be surprising that the top tech companies will ask about your SQL skills during an interview. Here, we cover the key concepts tested so you can best prepare for your next data science interview.
Amazon, Data Science, Facebook, Google, Interview Questions, SQL
- Transform speech into knowledge with Huggingface/Facebook AI and expert.ai - Sep 28, 2021.
Speech2Data is a blend of open source and free-to-use AI models and technologies powered by Huggingface, Facebook AI and expert.ai. Learn more here.
Expert.ai, Facebook, Hugging Face, Speech Recognition
- Facebook Open Sources a Chatbot That Can Discuss Any Topic - Jul 27, 2021.
The new version expands the capabilities of its predecessor building a much more natural conversational experience.
Chatbot, Facebook, NLP, Open Source
- Facebook Launches One of the Toughest Reinforcement Learning Challenges in History - Jun 15, 2021.
The FAIR team just launched the NetHack Challenge as part of the upcoming NeurIPS 2021 competition. The objective is to test new RL ideas using a one of the toughest game environments in the world.
Challenge, Facebook, Reinforcement Learning
- Facebook Open Sources ReBeL, a New Reinforcement Learning Agent - Dec 14, 2020.
The new model tries to recreate the reinforcement learning and search methods used by AlphaZero in imperfect information scenarios.
Agents, AI, Facebook, Open Source, Reinforcement Learning
- Remembering Pluribus: The Techniques that Facebook Used to Master World’s Most Difficult Poker Game - Dec 1, 2020.
Pluribus used incredibly simple AI methods to set new records in six-player no-limit Texas Hold’em poker. How did it do it?
AI, Facebook, Poker
Facebook Open Sourced New Frameworks to Advance Deep Learning Research - Nov 17, 2020.
Polygames, PyTorch3D and HiPlot are the new additions to Facebook’s open source deep learning stack.
Deep Learning, Facebook, Open Source, PyTorch, Research
- Facebook Uses Bayesian Optimization to Conduct Better Experiments in Machine Learning Models - Aug 10, 2020.
A research from Facebook proposes a Beyasian optimization method to run A/B tests in machine learning models.
Bayesian, Facebook, Machine Learning, Modeling, Optimization
- Prepare for a Long Battle against Deepfakes - Feb 21, 2020.
While deepfakes threaten to destroy our perception of reality, the tech giants are throwing down the gauntlet and working to enhance the state of the art in combating doctored videos and images.
AI, Crime, Deepfakes, Facebook, Google, Politics, Trends, Twitter
Open Source Projects by Google, Uber and Facebook for Data Science and AI - Nov 28, 2019.
Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public.
Advice, AI, Data Science, Data Scientist, Data Visualization, Deep Learning, Facebook, Google, Open Source, Python, Uber
- Facebook Adds This New Framework to It’s Reinforcement Learning Arsenal - Nov 11, 2019.
ReAgent is a new framework that streamlines the implementation of reasoning systems.
Facebook, Reinforcement Learning
Facebook Has Been Quietly Open Sourcing Some Amazing Deep Learning Capabilities for PyTorch - Nov 4, 2019.
The new release of PyTorch includes some impressive open source projects for deep learning researchers and developers.
Deep Learning, Facebook, PyTorch
- Inside Pluribus: Facebook’s New AI That Just Mastered the World’s Most Difficult Poker Game - Aug 8, 2019.
The reasons why Pluribus represents a major breakthrough in AI systems might result confusing to many readers. After all, in recent years AI researchers have made tremendous progress across different complex games. However, six-player, no-limit Texas Hold’em still remains one of the most elusive challenges for AI systems.
AI, Facebook, Poker
- Neural Code Search: How Facebook Uses Neural Networks to Help Developers Search for Code Snippets - Jul 24, 2019.
Developers are always searching for answers to questions about their code. But how do they ask the right questions? Facebook is creating new NLP neural networks to help search code repositories that may advance information retrieval algorithms.
Facebook, Information Retrieval, Natural Language Processing, Neural Networks, NLP, Programming
- The 6 Most Useful Machine Learning Projects of 2018 - Jan 15, 2019.
Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.
Automated Machine Learning, Facebook, fast.ai, Google, Keras, Machine Learning, Object Detection, Python, Reinforcement Learning, Word Embeddings
- Sales Forecasting Using Facebook’s Prophet - Nov 28, 2018.
In this tutorial we’ll use Prophet, a package developed by Facebook to show how one can achieve this.
Facebook, Python, Sales, Time Series
- Implementing Deep Learning Methods and Feature Engineering for Text Data: FastText - May 1, 2018.
Overall, FastText is a framework for learning word representations and also performing robust, fast and accurate text classification. The framework is open-sourced by Facebook on GitHub.
Facebook, Feature Engineering, NLP, Python
6 Interesting Things You Can Do with Python on Facebook Data - Jun 6, 2017.
Facebook has a huge amount of data that is available for you to explore, you can do many things with this data. I will be sharing my experience with you on how you can use the Facebook Graph API for analysis with Python.
Facebook, Pandas, Python
- Revenue per Employee: golden ratio, or red herring? - Jan 4, 2017.
There is growing support for revenue per employee as one of the most underrated metrics available for assessing business performance in a crowded marketplace.
Apple, Facebook, Google, Hiring, Workforce Analytics
- Top 10 Facebook Groups for Big Data, Data Science, and Machine Learning - Nov 23, 2016.
Social media now not only shares friendship connections or photos of “selfies” but also spreads from political media to science information. Social network members are tending to more eagerly learn about big data, data science and machine learning through groups. We review the ten largest Facebook groups in this area.
Big Data, Data Science, Facebook, Machine Learning
- Cartoon: Facebook data science experiments and Cats - Aug 8, 2016.
In honor of International Cat Day, we revisit KDnuggets cartoon that looks at the Facebook data science experiment on emotion manipulation and the importance of happy kittens.
Cartoon, Cats, Data Science, Facebook
- What is Your Data Worth? On LinkedIn, Microsoft, and the Value of User Data - Jun 20, 2016.
The recent announcement of Microsoft’s acquisition of LinkedIn has raised many questions about how Microsoft will monetize this data. We examine LinkedIn value per user and compare to Google, Facebook, Yahoo, and Twitter.
Business Value, Facebook, Google, LinkedIn, Microsoft, Yahoo
- How Small is the World, Really? - Feb 22, 2016.
Social network analysis is back in the news again, with a recent Facebook project which determined that there are an average of 3.5 intermediaries between any 2 Facebook users. But this is different than "6 degrees of separation." Read on to find out why, and how.
Duncan Watts, Facebook, Small World
- Facebook Open Sources deep-learning modules for Torch - Feb 9, 2015.
We review Facebook recently released Torch module for Deep Learning, which helps researchers train large scale convolutional neural networks for image recognition, natural language processing and other AI applications.
Artificial Intelligence, Deep Learning, Facebook, GPU, Neural Networks, NYU, Ran Bi, Torch, Yann LeCun
- KDnuggets Exclusive: Interview with Yann LeCun, Deep Learning Expert, Director of Facebook AI Lab - Feb 20, 2014.
We discuss what enabled Deep Learning to achieve remarkable successes recently, his argument with Vapnik about (deep) neural nets vs kernel (support vector) machines, and what kind of AI can we expect from Facebook.
Andrew Ng, Deep Learning, Facebook, Interview, NYU, Support Vector Machines, Vladimir Vapnik, Yann LeCun
- Deep Learning Wins Dogs vs Cats competition on Kaggle - Feb 5, 2014.
A Deep learning expert wins Kaggle Dogs vs Cats image competition with an almost perfect result.
Cats, Competition, convnet, Deep Learning, Dogs, Facebook, Kaggle
