We crossed 100,000 public AI models on the @huggingface hub available for free to all. Thank you to the whole community of contributors. Proud to make ML more open & collaborative!
MACHINE LEARNING
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Docker Now Available for Spaces: Deploy ML Apps Easily
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You can now run Docker with Spaces! What does that mean? @elixirlang Phoenix, Dash, FastAPI, Shiny, and your favorite ML tools can now be used with Spaces! What will you build?
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Learning Recommendation Systems with Movie Dataset
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Recommendation systems are one of the most widely used topics of machine learning. Some of your favorite applications use a recommendation system at their core. Here is a great dataset to learn: https://
kaggle.com/rounakbanik/th
e-movies-dataset
… 45,000+ movies. 26M ratings from over 270,000 users. -
Groq AI Platform Accelerates Deep Learning for Fusion Energy Research
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Researchers are leveraging the @GroqInc #AI platform at the @argonne_lcf
’s AI Testbed to accelerate deep learning-guided investigations aimed at informing the operation of future #fusionenergy devices. Read more here: https://
groq.link/anltok -
Automate Text Analysis with IBM Watson NLP Library
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Reading through endless amounts of comments about your business can get tiring With #IBMWatson's #NLP Library, automatically gather, process and classify large datasets of text by emotion. Learn how here: https://
ibm.co/3BwoDgn via @IBMDeveloper —–
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Machine Learning Project: Detecting Outliers in Sensor Data
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Full Machine Learning Project — Detecting Outliers in Sensor Data (Part 4) #DataScience
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Women in AI: Breaking Down Bias with Nicole Foster
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Break down #bias – Women in #ArtificialIntelligence Ep1: Nicole Foster Dir Global #AI #ML @awscloud https://
youtu.be/_fW90t3MS94 @YouTube @1OFFGINGER @DeepLearn007 @DigitalColmer #STEM #womenintech @sandy_carter @NolwennGer @moingshaikh @ipfconline1 @TmanSpeaks @EstelaMandela -
Senior Executives Prioritize AI Machine Learning Automation Investment
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73% of senior executives see AI, machine learning, and automation as important areas to maintain or increase investment in
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Tokenizer Libraries Essential Features for NLP Tasks
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it's not really doing the same thing, they are doing encoding only while the tokenizer lib does a lot of work keeping track of offset between the original string and the final tokens, truncating, etc. this is needed to support various model hub task like QA, etc
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Local Optima: Not a Problem for Generalization in Machine Learning
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Reminder: local optima were never a problem in machine learning, where the goal is generalization, not fitting the training data perfectly.