The Best #DataMining Books Based on Reviews. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Best-Mining-Re
views
…
MACHINE LEARNING
-
Best Data Mining Books Reviews for Analytics Professionals
By
–
-
Best Programming Books for Data Science and Automation
By
–
Best Books For Beginners And Advanced Experts. #BigData #Analytics #DataScience #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/Best-Python-Au
tomation
… -
Data Strategy for Digital Business Transformation Understanding
By
–
Understanding #Data Strategy for Charting the Course of #Digital #Business Transformation
by @antgrasso @DeltalogiX Read more: https://
buff.ly/469UXCA #ArtificialIntelligence #MI #DataScience cc: @pbalakrishnarao @yvesmulkers @kuriharan -
Keras with PyTorch matches vanilla PyTorch performance when compiled
By
–
That's because Keras + torch is not compiled by default whereas the native torch models are compiled. Keras+torch has the same performance as vanilla torch.
-
Sakana AI’s cost-efficient model merging with limited GPU resources
By
–
“At the time, @SakanaAILabs only had 16 GPUs, which cost $30K per month. That’s pennies compared to the $100M+ it took to train advanced models like GPT-4… Model merging isn’t perfect though. @SakanaAILabs is one of only a few firms who have attempted to automate this process.”
-
Framework Comparison: JAX, PyTorch, TensorFlow Development Experience
By
–
I agree with your point that people hours are worth more than GPU hours (except for large-scale training runs or inference workloads of course…). Having been developing in all three frameworks (torch, TF, JAX) extensively over the past year, I've personally found JAX to be the
-
Keras torch backend performance overhead compilation optimization
By
–
Keras adds virtually no overhead over torch actually… the torch backend numbers are much worse simply because Keras + torch is not compiled by default, whereas the native torch models were compiled with the best compilation config we found. Keras + torch has essentially the
-
RAG Implementation: Embedding Passages and Queries Separately
By
–
Being able to embed your content as "passage" but questions people ask about it as "query" is useful for implementing RAG – a user's question might not naturally embed to a similar location as content that answers that question, this trick helps fix that
-
Embedding Models with Content Mode Separation
By
–
What embedding models are there with separation between different modes of content? I know E5-Large-V2 has that ("passage" vs "query"), and @nomic_ai have "search_query", "search_document", "clustering", "classification" https://
docs.nomic.ai/reference/endp
oints/nomic-embed-text
… Any other good examples? -
3D Gaussian Splats Training Locally on Nvidia GPU
By
–
It never gets old to see your 3D scans brought to life right in front of you, especially when you're doing the training locally.
— Bilawal Sidhu (@bilawalsidhu) 31 mars 2024
This is a 3D Gaussian Splat with about 30,000 iterations trained on my Nvidia GPU using a software called Postshot.
With it you can train splats and… pic.twitter.com/89T4dLoto6It never gets old to see your 3D scans brought to life right in front of you, especially when you're doing the training locally. This is a 3D Gaussian Splat with about 30,000 iterations trained on my Nvidia GPU using a software called Postshot. With it you can train splats and