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How Artificial intelligence and Machine learning used by Facebook | AI and ML in 2020 in this video, we will discuss the innovative machine learning and artificial technology used by Facebook. Facebook started out as a platform for connecting people in different parts of the world using images, videos, and text. With more than 4 billion people using Facebook every month, Facebook has put in a lot of hard work getting these users engaged and connected and this is where machine learning Deep Learning and artificial intelligence comes in. Let's have a look at how Facebook is using machine learning and artificial intelligence tool practically

Deep Learning at Facebook - Yann LeCunn | Lecture Series on AI #3 In this talk, Yann dives into the history of deep learning, and what deep learning looks like at Facebook.

How to get into AI + Facebook AI reading list (the best resources) Hi, I'm Oleksii and I work at Facebook AI Research. The list of the best resources to learn ML and AI is right here. Yann LeCun is a VP & Chief AI Scientist at Facebook, and Silver Professor of CS and Neural Science at NYU. Previously, Yann was the founding Director of Facebook AI Research and of the NYU Center for Data Science. He received a PhD in Computer Science from Université P&M Curie (Paris). After a postdoc at the University of Toronto, Yann joined AT&T Bell Labs, and became head of Image Processing Research at AT&T Labs in 1996. He joined NYU in 2003 and Facebook in 2013. Yann’s current interests include AI, machine learning, computer vision, mobile robotics, and computational neuroscience. He is a member of the National Academy of Engineering. ==== Stage 1 ==== -Theory: Linear Algebra, Statistics, Theory of Probability (any courses/books will work) -Practice: Basic Python skills, NumPy ==== Stage 2 ==== -Theory: Online courses / books on ML and Deep Learning; Machine Learning course by Andrew Ng, Coursera; Deep Learning Book, Ian Goodfellow; Stanford CS231n course; Recent MIT course: http://introtodeeplearning.com/; https://www.fast.ai/ -Practice: Pandas, PyTorch, Tensorflow and "official" tutorials (form their websites) ==== Stage 3 ==== -Theory: "Classic" papers, e.g. AlexNet, ResNet, BERT; Reading lists: http://deeplearning.net/reading-list/ https://github.com/ujjwalkarn/Machine... https://github.com/floodsung/Deep-Lea... MILA reading list: https://docs.google.com/document/d/1I... Andrej Karpathy's blog: http://karpathy.github.io/ -Practice: Random tutorials on the internet, Advanced tutorials, replicating papers ==== Stage 4 ==== -Theory: New paper on your topic, conferences, ArXiv, mailing lists and news channels; https://distill.pub/; Sebastian Rudder: http://newsletter.ruder.io/ ; THE BATCH: https://www.deeplearning.ai/thebatch/ -Practice: Working on toy-projects; Contributing to open-source projects; Getting an internship; Trying to get hands-on experience in any way possible === Never Stop === These all will be usefull for anyone, engineer or researcher. Also try to concentrate on one particular topic and get an expert in it first, it will be much easier to accomplish and will open you opportunities right away. Don't try to become an expert in everything and also do not neglect "not prestigious" experience. Any experience is very valuable.

Realistic Day in the Life of AI/ML Researcher at Facebook Ever wondered what actually #AI/#ML Researcher/#Engineer 's do? Let me show you a sneak peek of one of the typical workdays. I bet it is very similar at Google, Microsoft, Amazon, DeepMind, Stanford, Berkeley, MIT, and other big research labs (that's how you feed youtube algos with tags, lol). Obviously, what Researchers/Engineers do most of the time is training deep learning models written in PyTorch/TensorFlow, 👉writing research papers👈 and sometimes even reading papers. This one sentence actually contains more info than the whole video.