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We need better #NLP datasets now more than ever!

A video dissection of the paper from FAIR on program translation from one language to another in an unsupervised manner: https://t.co/Kr7hZ60IQs

This is the first time in human history @NASA_Astronauts have entered the @Space_Station from a commercially-made spacecraft. @AstroBehnken and @Astro_Doug have finally arrived to the orbiting laboratory in @SpaceX's Dragon Endeavour spacecraft.

We took on the Cloud Setup Challenge... and we won🏆. Handily, if we do say so ourselves. We took @quaesita handle’s challenge and set up a Jupyter + TensorFlow work station in 44 SECONDS. Think you can beat us? 🔥 Share your results at #cloudchallenge! https://t.co/ENASQun7Zq

Today, we are officially releasing the cloud-native 🔍neural search framework powered by state-of-the-art AI and deep learning today! It's time to think out of the [Text] box! Unleash your curiosity and find out more by clicking below!👇 https://t.co/tSIOOZE9cH

After the recent post on text similarity search using pretrained LMs, I received lots of really good questions but one question that was common is when to use which NLP model. Here is another impressive blog post by @cathalhoran on the topic. https://t.co/vKq3hiEesz

A new #humansofml interview, featuring @antoniogulli, is live, in which we discuss the role of intuition in data science, the future of cloud platforms, and whether humans actually know what intelligence is with a true pioneer in the field. https://t.co/RwfHgnLzei

Many companies struggle to get impact from machine learning projects, so @mikeloukides and I wrote this guide: "What you need to know about product management for AI"

And that, friends, ends this Thread of Threads of the best machine learning & deep learning books. May these 17 recently-published books (+ 4 more that will be released later this year) teach you all you seek to know.

BONUS ENTRY 4: For readers who want to optimize the human-computer interaction of ML systems: Human-in-the-Loop Machine Learning by @WWRob

BONUS ENTRY 3: We truly cannot wait to read this upcoming deep dive on ML interviews from a prolific writer on the subject: Machine Learning Interviews Book by @chipro

BONUS ENTRY 2: The sequel to The Hundred-Page Machine Learning Book, this title will focus on the engineering side of ML projects: The Machine Learning Engineering Book by @burkov

BONUS ENTRY 1: One of the most-anticipated releases of 2020 (at least in the machine-learning world): Deep Learning for Coders with @fastdotai and @PyTorch : AI Applications Without a PhD by @jeremyphoward & @GuggerSylvain

17) Generative models are the future in terms of empowering creatives, and this book artfully explains how they work: Generative Deep Learning by David Foster

16) This title is THE free online book on deep learning. Period. Neural Networks and Deep Learning by @michael_nielsen

15) With a subtitle "a guide for making black box models explainable," doesn’t this book automatically sound useful? Interpretable Machine Learning by @ChristophMolnar

14) For practitioners who want to understand recent breakthroughs in everything from debugging to fairness to interpretability: An Introduction to Machine Learning Interpretability (2nd Edition) by @jpatrickhall & @Navdeep_Gill_ (@h2oai)

13) Catchy title + years of wisdom from leading deep learning teams at @Baidu_Inc and @Google Brain + practical resources = Machine Learning Yearning by @AndrewYNg

12) Exploring the #AIDebate between symbolic and connectionist AI with actionable research is: Rebooting AI by @GaryMarcus & Ernest Davis