Complete (and Comprehensive!) Series of Implemented Applied #MachineLearning Projects => 60+ implemented projects with code: https://
naina0405.substack.com/p/complete-ser
ies-implemented-applied-611
… by @NainaChaturved8 ————
#DataScience #DataScientists #AI #Coding #Python #ML #Algorithms #NeuralNetworks #DataViz #LinearAlgebra
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60+ Implemented Applied Machine Learning Projects with Code
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Open Source Emerges as Common Enemy in Rights vs Weights Battle
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In the battle between rights owners and weights owners, open source is the common enemy
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Essential Python Data Manipulation and Analysis Handbook
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Here's the book for you: http://
amzn.to/3wJJaMy by @wesmckinn Definitive handbook for manipulating, processing, cleaning, & crunching datasets in #Python. Updated 3rd edition is packed with practical case studies that show how to solve a broad set of data analysis problems. -
PyTorch Recipes: Build, Train, Deploy Neural Network Models
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#PyTorch Recipes — Problem-Solution Approach to Build, Train and Deploy Neural Network Models: http://
amzn.to/48bDScW by @pradmishra1 CODE SNIPPETS: https://
pytorch.org/tutorials/begi
nner/pytorch_with_examples.html
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#Coding #Python #DataScientists #AI #MachineLearning #DeepLearning #NeuralNetworks #DataScience -
Applying Math with Python: 70+ Practical Recipes for Computational Problems
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Applying Math with #Python, with 70+ practical recipes for solving real-world #ComputationalMath problems (2nd Edition): http://
amzn.to/49mQePN ————
#Mathematics #ComputationalScience #Coding #DataScience #AppliedMath #Simulation #NumPy #SciPy #Statistics -
Master LLM Fine-Tuning: A Comprehensive Guide to Benefits and Implementation
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Ready to master #LLM fine-tuning? Check out our definitive guide: Benefits of #FineTuning and when to do it How to prepare your #data set What it takes to manage training + serving #infra How to reliably and #efficiently fine-tune
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9 Best Practice Patterns for Effective User Story Splitting
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Take a look at these 9 best practice patterns for splitting a user story effectively. @eiki234 includes: 1. End-to-end first 2. Business rule variations 3. Major combination 4. Simple/complex 5. Variations in data 6. UI/UX separation 7. Defer performance
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Building RAG Chatbots with LangChain and OpenAI
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Check out this detailed walkthrough on building a chatbot with RAG, using LangChain and OpenAI! @qdrant_engine
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Client-Side PDF Chat with RAG and Vector Store
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🏠 Fully Client-Side Chat Over Documents
— LangChain (@LangChain) 11 mars 2024
This app reads the content of an uploaded PDF, chunks it, adds it to a vector store, and performs RAG – all client side.
By our own @Hacubu (trending on GitHub!)https://t.co/oolcWyoubV pic.twitter.com/RFZsv09J4wFully Client-Side Chat Over Documents This app reads the content of an uploaded PDF, chunks it, adds it to a vector store, and performs RAG – all client side. By our own @Hacubu (trending on GitHub!) https://
github.com/jacoblee93/ful
ly-local-pdf-chatbot
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Improving LLM Reliability Through Evaluation Driven Development
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Iterating Towards LLM Reliability with Evaluation Driven Development If you've interacted with the LangChain GitHub repo, you may have noticed a helpful GitHub bot called Dosu You may have also noticed that it's been improving over time! In this blog @devstein64 dives into