Neural Machine Translation by Jointly Learning to Align and Translate Bahdanau et al.: https://
arxiv.org/abs/1409.0473 #Artificialintelligence #DeepLearning #Transformers
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Attention Mechanism in Neural Machine Translation Explained
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Transform Your Organization into an AI-First Company with Abacus
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You can turn your organization into an AI-first company, We built the world's ONLY AI-assisted data science and MLOps platform. Take a look: https://
abacus.ai -
Harvard’s Free Python AI Introduction Course
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Introduction on Artificial Intelligence with Python by Harvard University! 100% FREE course! Course link https://
youtu.be/5NgNicANyqM #OPENAI #GenerativeAI #LLM #ChatGPT #LLM #DataScientist #Tech #BigData #Analytics #innovation #Crypto #MachineLearning #TensorFlow #coding -
Microsoft releases optimized Llama 2 ONNX model version
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Llama-2-Onnx : an optimized version of the Llama 2 model https://
github.com/microsoft/Llam
a-2-Onnx?s=09
… V/ @_akhaliq #OPENAI #GenerativeAI #LLM #ChatGPT #LLM #DataScientist #Tech #BigData #Analytics #innovation #Crypto #MachineLearning #TensorFlow #coding #IoT #100DaysOfCode #NodeJS #Python -
Generative AI with LangChain: Tools and Techniques
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Generative AI with LangChain! @benji1a #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/Gen-AI-LangCha
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AI with Python Cookbook: Essential Guide for Data Scientists
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AI with Python Cookbook! @benji1a #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/PyCookbook -
Harness Launches Generative AI Assistant for Developer Efficiency
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Harness releases generative AI assistant to help increase developer efficiency
#AI #AIio #BigData #ML #NLU #Futureofwork @ahier @guzmand
@DavidBrin @denisegarth @dez_blanchfield @diioannid @DioFavatas @gerald_bader http://
ow.ly/qx3k30swABJ -
LangSmith Parallel Execution Optimization in Runnable Maps
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If we look at the LangSmith trace, we can see that the overall RunnableMap step is less than the sum of its components (because those components are run in parallel
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LangChain Expression Language enables parallel operations optimization
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If you do these operations sequentially, they can start to add up That's why it's important to do them in parallel One nice attribute of the LangChain Expression Language is it tries to do things in parallel where ever possible
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LangChain RunnableMap Parallel Operations Guide
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In this case, we have a `RunnableMap` (the dictionary) For these operations, they are run in parallel See our guide on that here: https://
python.langchain.com/docs/guides/ex
pression_language/interface#parallelism
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