While the future remains a mystery, there are moments when we can anticipate its shape. The field of #taxanalytics is currently undergoing tremendous changes, and it seems that #AI is ready to transform it completely. https://
ow.ly/9tYw50QyuMF
MACHINE LEARNING
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AI Transforms Tax Analytics: Future of Financial Analysis
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Self-Reflective RAG Enhancement Using LangGraph
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Self-Reflective RAG using LangGraph Self-reflection can enhance RAG, enabling correction of poor quality retrieval or generations. Several recent papers focus on this theme, but implementing the ideas can be tricky. Here is a video and two cookbooks that show how to engineer
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Optimizing Transformer Training on Single GPU with HuggingFace
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what are some easy ways for a normal person like me to speed up training a transformer on a single GPU? i'm using huggingface T5 implementation + defaults. i know about FlashAttention and BetterTransformer and torch compile? will these all work together? is there anything else?
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LLM Leaderboards: Benchmarks as Targets Reveal Sensitivity
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When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards Alzahrani et al.: https://
arxiv.org/abs/2402.01781 #Artificialintelligence #DeepLearning #MachineLearning -
Year Wrap-Up: Python, Data Science, ML & LLM Content Sharing
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That's a wrap! Every day, I share and simply content around Python, Data Science, Machine Learning & Large Language Models. Find me → @Sumanth_077 Like/RT the first tweet and help this reach more people.
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Modin: A Python Library 10x Faster Than Pandas
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Pandas is one of the most important libraries for Data Science.
— Sumanth (@Sumanth_077) 7 février 2024
But when working with larger datasets it becomes really slow and runs out of memory
Introducing Modin a python library which is 10x faster than Pandas🔥
Thread🧵👇 pic.twitter.com/Wl09g5OmdKPandas is one of the most important libraries for Data Science. But when working with larger datasets it becomes really slow and runs out of memory Introducing Modin a python library which is 10x faster than Pandas Thread
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Modin: Accelerating Pandas on Large Datasets
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Modin works well on larger datasets, where pandas becomes slow or runs out of memory Check the below example, Pandas (left) and Modin (right) performing the same pandas operations on a 2GB dataset. The only difference between the two examples is the import statement:
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Run UI and Model Training Locally with AutoTrain Advanced
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Run UI & Trainings locally NOW! No-code, state of the art model training. “`
$ pip install autotrain-advanced
$ export HF_TOKEN=your_hf_write_token
$ autotrain app
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Carnegie Mellon and ETH Zurich Advance Fast Robot Navigation
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Researchers at Carnegie Melon and ETH Zurich are helping robots be agile, fast, and safe.
— Rowan Cheung (@rowancheung) 7 février 2024
A new framework allows for navigation of cluttered spaces at speeds of nearly 7 MPH without collisions.
An important skillset as robotic tasks become more complex. pic.twitter.com/hiiucIaxyvResearchers at Carnegie Melon and ETH Zurich are helping robots be agile, fast, and safe. A new framework allows for navigation of cluttered spaces at speeds of nearly 7 MPH without collisions. An important skillset as robotic tasks become more complex.
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TensorFlow GNN 1.0 Released for Production-Scale Graph Neural Networks
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Graph neural networks (GNNs) have emerged as a powerful technique to leverage the uniquely heterogenous data from graphs. So, we are excited to announce the release of TensorFlow GNN 1.0 (TF-GNN), a production-tested library for building GNNs at scale → https://t.co/sJ6LDVyHuR pic.twitter.com/5ZM8gEDE6q
— Google AI (@GoogleAI) 6 février 2024Graph neural networks (GNNs) have emerged as a powerful technique to leverage the uniquely heterogenous data from graphs. So, we are excited to announce the release of TensorFlow GNN 1.0 (TF-GNN), a production-tested library for building GNNs at scale → https://
goo.gle/3w8ogZC