The book “#AlgorithmicTrading with Python” discusses modern quant trading methods in #Python with a heavy focus on PANDAS, Numpy, and scikit-learn: http://
amzn.to/3uvYlKk by @__chrisconlan__ Get free code from the book here: https://
github.com/chrisconlan/al
gorithmic-trading-with-python
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MACHINE LEARNING
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Algorithmic Trading with Python: Modern Quant Methods and Free Code
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Deep Learning Anomaly Detection for Fast Data Response
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#AnomalyDetection by @AbacusAI uses leading-edge #DeepLearning models to spot anomalies in your data and to enable fast informed response: https://
abacus.ai/anomalydetecti
on
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#AI #MachineLearning #BigData #Analytics #DataScience #CDO #DataScientists #DataQuality -
BERT masked token prediction and LLM next-word prediction clarification
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Hope you are enjoying the book! And thanks for sharing your notes! Btw regarding your nits, I introduced the masked token prediction task of BERT in Fig 1.5 and even refer to BERT-like "LLMs", but you are right, I mostly equate LLMs with "next-word prediction" for simplicity's
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Training LLMs to Outperform Humans in Mathematics
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Now you can train LLMs to be much better than you at math (and get disappointed in yourself even more )
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GH200 H200 H100 GPUs Now Available via Hugging Face Lambda
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You can now get on-demand GH200/ H200/ H100 directly via your @huggingface account, powered by @LambdaAPI What's your excuse to not learn/ practice this holidays?
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Geometry-grounded Representation Learning Workshop Proceedings Published
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Volume 251 Proceedings of Geometry-grounded Representation Learning and Generative Modeling Workshop Is now available on PMLR
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Volume 252 of ML for Healthcare Conference Proceedings Published on PMLR
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Volume 252 Proceedings of the ML for Healthcare Conference Is now available on PMLR.
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Using model diversity for better benchmark evaluation
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I think this makes sense mostly for tasks where you think models have relevant non-shared knowledge. E.g. it’d be good for aidanbench tasks where you want as much coherent diversity as possible — models should be more inspired by other models than by copies of themselves
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Chollet launches consulting profile on Params platform
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I started a profile on Params, where you can book a consulting call with me to chat about recommender system plans: https://
params.com/@fchollet Params is a platform for open-source devs to publish project templates — project users can then book calls with template authors. -
Sparse Autoencoders Explained for LLM Interpretability
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An Intuitive Explanation of Sparse Autoencoders for LLM Interpretability https://
bit.ly/3ODlDVK
#AI #MachineLearning #DeepLearning #LLMs #DataScience