@ramikrispin@mstdn.social
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ramikrispin

@ramikrispin@mstdn.social

Data science and engineering senior manager at ο£Ώ | #rstats & #Python | πŸ“¦ dev | ❀️ time-series analysis & forecasting | Author. Opinions are my own | https://linktr.ee/ramikrispin

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ramikrispin, to datascience
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Forecasting Time Series with Gradient Boosting ❀️

The skforecast Python 🐍 library provides ML applications for time series forecasting using different regression models from the scikit-learn library. Here is a tutorial by JoaquΓ­n Amat Rodrigo and Javier Escobar Ortiz for time series forecasting with the skforecast using XGBoost, LightGBM, Scikit-learn, and CatBoost models πŸš€.

πŸ“–πŸ”—: https://cienciadedatos.net/documentos/py39-forecasting-time-series-with-skforecast-xgboost-lightgbm-catboost

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ramikrispin, to llm
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I am excited to present at the OSDC East conference next week about using LLM to create language to SQL code generator.

https://odsc.com/speakers/data-automation-with-llm/

ramikrispin, to llm
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Llama 3 is already available on Ollama πŸš€πŸ‘‡πŸΌ

https://ollama.com/library/llama3

#llm #llama3 #ollama #python #DataScience

ramikrispin, to llm
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(1/3) Llama 3 is out! πŸš€

Meta released today Llama 3, the next generation of the Llama model. LLama 3 is a state-of-the-art open-source large language model. Here are some of the key features of the model: πŸ§΅πŸ‘‡πŸΌ

video/mp4

ramikrispin,
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(2/3) βœ… The new model comes with integration into the core cloud providers such as AWS, GCP, Azure, Databricks, Hugging Face, etc.
βœ… The model supports a variety of hardware architectures, such as AMD, AWS, Dell, Intel, NVIDIA, and Qualcomm.
βœ… Supporting safety and cyber tools
βœ… The model comes in two versions - 7B and 80B

ramikrispin,
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(3/3)
More details are available on the release post πŸ‘‡πŸΌ
https://ai.meta.com/blog/meta-llama-3/

Playground: https://www.meta.ai/

ramikrispin, to llm
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RAG from Scratch with LangChain πŸ¦œπŸ‘‡πŸΌ

FreeCodeCamp released today a new course on building RAG from scratch with LangChain. The course, which is by Lance Martin from LangChain, focuses on the foundations of Retrieval Augmented Generation (RAG).

Course πŸ“½οΈ: https://www.youtube.com/watch?v=sVcwVQRHIc8
Code πŸ”—: https://github.com/langchain-ai/rag-from-scratch

#llm #rag #python #langchain #mlops

ramikrispin, to datascience
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New release to Ollama πŸŽ‰

A major release to Ollama - version 0.1.32 is out. The new version includes:
βœ… Improvement of the GPU utilization and memory management to increase performance and reduce error rate
βœ… Increase performance on Mac by scheduling large models between GPU and CPU
βœ… Introduce native AI support in Supabase edge functions

More details on the release notes πŸ‘‡πŸΌ
https://github.com/ollama/ollama/releases

Image credit: release notes

ramikrispin, to datascience
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Gemini API Cookbook πŸš€

Google released a new repo with a collection of guides and examples for the Gemini API. This includes a set of guides for prompt engineering and examples of the API features πŸ‘‡πŸΌ

πŸ”— https://github.com/google-gemini/cookbook

ramikrispin, to datascience
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(1/3) Learn R Through Examples πŸš€πŸ‘‡πŸΌ

The Learn R Through Examples by Xijin Ge, Jianli Qi, and Rong Fan provides an introduction to data analysis with R. The book covers the core topics of data analysis using different datasets, from simple and clean datasets to messy and big datasets. πŸ§΅πŸ‘‡πŸΌ

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ramikrispin,
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(2/3) This includes the following topics:
βœ… Working with data frames
βœ… Data visualization with base R and ggplot2
βœ… Data structures
βœ… Summary statistics and correlation analysis
βœ… Case studies - analyzing multiple datasets

ramikrispin,
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(3/3) Book πŸ“š: https://gexijin.github.io/learnR/index.html

Thanks to the authors for making this book available for free online! πŸ™πŸΌ

Image credit: from the book

ramikrispin,
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@AlanSill my go-to solution for parallelize execution in R is the mclapply - which enables running lapply function in parallel using multi CPUs:
https://www.rdocumentation.org/packages/parallel/versions/3.4.0/topics/mclapply

ramikrispin,
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@dzegpi I am not familiar about books or resources focusing on forecasting with tidymodels besides the work of Matt Dancho.

ramikrispin, to datascience
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Happy Sunday!

Backpropagation Calculus πŸš€πŸš€πŸš€

This short video by Grant Sanderson provides a great explanation of the math beyond the backpropagation algorithm using calculus πŸ‘‡πŸΌ

https://www.youtube.com/watch?v=tIeHLnjs5U8

ramikrispin, to datascience
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Hands-on Data Science: Complete your First Project πŸš€

This beginner crash course by Misra Turp provides an introduction to the foundations of data science by solving real-life examples. This includes the different steps of a data science project, from setting the environment to loading and analyzing the data using Python, git, Jupyter notebooks and other tools πŸ‘‡πŸΌ

πŸ“½οΈ: https://www.youtube.com/playlist?list=PLM8lYG2MzHmTgsYKLJtdKwf6tHVbui9eE

ramikrispin, to python
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(1/2) A new release to PyMC πŸš€πŸš€πŸš€

This week, PyMC version v5.13.0 was released. PyMC is one of the main 🐍 libraries for 𝐁𝐚𝐲𝐞𝐬𝐒𝐚𝐧 statistics ❀️. It provides a framework for probabilistic programming, enabling users to build models with a simple Python API and fit them using 𝐌𝐚𝐫𝐀𝐨𝐯 π‚π‘πšπ’π§ 𝐌𝐨𝐧𝐭𝐞 π‚πšπ«π₯𝐨 (MCMC) methods πŸš€.

The new release includes new features, bug fixes 🐞, and documentation improvements πŸ“–. More details on the release notes πŸ“ πŸ‘‡

ramikrispin,
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(2/2) π‹π’πœπžπ§π¬πž πŸͺͺ: Apache 2.0 πŸ¦„

Release notes πŸ“: https://github.com/pymc-devs/pymc/releases/tag/v5.13.0
Documentation πŸ“–: https://www.pymc.io/welcome.html

stevensanderson, to github
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ramikrispin,
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@stevensanderson what are the applications of this function?

ramikrispin, to python
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(1/2) Introduction To CS And Programming Using Python 🐍 - New MIT Course πŸš€πŸ‘‡πŸΌ

MIT released an introductory course for computer science by Dr. Ana Bell. This full semester course (26 lectures) focuses on the foundations of programming using Python. This is a beginner level and does not require previous programming experience.

ramikrispin,
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(2/2) The course covers the following topics:
βœ… Classes
βœ… Iteration and looping over items
βœ… Functions and objects
βœ… List, dictionary, and other data formats
βœ… Object-oriented programming

Lecture πŸ“½οΈ: https://www.youtube.com/playlist?list=PLUl4u3cNGP62A-ynp6v6-LGBCzeH3VAQB

ramikrispin, to python
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(1/2) Models Demystified - A Practical Guide from t-tests to Deep Learning πŸš€πŸ‘‡πŸΌ

The Models Demystified is a new book by Michael Clark and Seth Berry that focuses on the mechanizing of core data science algorithms. That includes the following topics:
βœ… Linear and logistic regression
βœ… Generalized Linear Models
βœ… Regularization methods
βœ… Model training approaches
βœ… Deep learning and neural networks
βœ… Causal Modeling

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ramikrispin,
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(2/2) The code examples are with both R and Python 🐍.

Book πŸ“š: https://m-clark.github.io/book-of-models/

Thanks to the authors for making this book available for free online! πŸ™πŸΌ

Image credit: from the book

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ramikrispin, to datascience
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Bandit Algorithms Book πŸš€πŸ‘‡πŸΌ

The Bandit Algorithms by Tor Lattimore and Prof. Csaba SzepesvΒ΄ari provides an introduction to the multi-armed bandit problem. This includes different approaches for solving this type of problems using stochastic, adversarial, and Bayesian frameworks.

Book πŸ“š: https://tor-lattimore.com/downloads/book/book.pdf

Thanks to the authors for making this book available for free online! πŸ™πŸΌ

Image credit: from the book

ramikrispin, to llm
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Production Monitoring & Automations of LLM with LangSmith πŸ¦œπŸ‘‡πŸΌ

LangChain released a crash course for LangSmith, their DevOps platform for deploying LLM applications into production. The course covers topics such as:
βœ… LLM applications monitoring
βœ… Setting automation
βœ… Performance monitoring

πŸ“½οΈ: https://www.youtube.com/playlist?list=PLfaIDFEXuae0bYV1_60f0aiM0qI7e1zSf

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