@ramikrispin@mstdn.social
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ramikrispin

@ramikrispin@mstdn.social

Data science and engineering senior manager at  | #rstats & #Python | 📦 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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(1/2) End To End Data Science With R 🚀

The End To End Data Science With R is a new book by Rene Essomba. The book, as the name implies, focuses on the core data science applications using R ❤️. This book covers the following topics:
✅ Exploratory data analysis
✅ Data visualization
✅ Supervised learning
✅ Unsupervised learning
✅ Time series
✅ Natural language processing
✅ Image classification

#RStats #DataScience #MachineLearning #dataviz #data

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ramikrispin,
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(2/2) The book is open and available online for free:
https://e2e-ds-r.franckess.com/

Image credit: Book

Thanks to the author for making this book open and free! 🙏🏼

ramikrispin, to machinelearning
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MLX Examples 🚀

The MLX is Apple's framework for machine learning applications on Apple silicon. The MLX examples repository provides a set of examples for using the MLX framework. This includes examples of:
✅ Text models such as transformer, Llama, Mistral, and Phi-2 models
✅ Image models such as Stable Diffusion
✅ Audio and speech recognition with OpenAI's Whisper
✅ Support for some Hugging Face models

🔗 https://github.com/ml-explore/mlx-examples

ramikrispin,
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@Lobrien @BenjaminHan The corenet is deep learning application where the MLX is array framework for high performance on Apple silicon. This mean that if you are using mac with M1-3 CPU it should perform better when using MLX on the backend (did not test it myself)

ramikrispin, to python
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This weekend working on a fun project combining AirFlow, MLflow, and Darts 😎

ramikrispin,
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@ramblingsteve Airflow is used to orchestrate the pipeline (data automation, model refresh), and MLflow is used for model experiments and tracking model performance.

ramikrispin, to python
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(1/2) Hands-On Mathematical Optimization with Python 🚀

The Hands-On Mathematical Optimization with Python book by Krzysztof Postek, Alessandro Zocca, Joaquim Gromicho, and Jeffrey Kantor provides the foundation for mathematical optimization. As the name implies, the book is hands-on with Python examples, mainly using Pyomo.

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ramikrispin,
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(2/2) The book covers different optimization methods, such as:
✅ Mathematical Optimization
✅ Linear Optimization
✅ Network Optimization
✅ Convex Optimization
✅ Stochastic Optimization

The book is open and available online.

📖🔗: https://mobook.github.io/MO-book/intro.html

Thanks to the authors for making it available for free! 🙏🏼

ramikrispin, to datascience
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(1/3) Introduction to Data Wrangler 🚀

Data Wrangler is a new Microsoft VScode extension for data exploratory analysis. It supports Python 🐍 and Pandas 🐼 DataFrame objects and is integrated into VScode Jupyter Notebooks. Here are some of the functionalities of Data Wrangler:
✅ Data review
✅ Column filtering
✅ Summary statistics
✅ Data cleaning and transformation
✅ Hadeling missing values
✅ Creating new fields

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ramikrispin,
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(2/3) Once finished processing the data, it creates a reproducible Python code ❤️

More details 👇🏼
https://marketplace.visualstudio.com/items?itemName=ms-toolsai.datawrangler

Image credit: Extension documentation

ramikrispin,
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(3/3) Here is a short video about the extension functionality 👇🏼

https://www.youtube.com/watch?v=5tWJVLF6PuA

ramikrispin, to ArtificialIntelligence
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(1/2) MIT Introduction to Deep Learning 🚀🚀🚀

MIT launched the 2024 edition of the Introduction to Deep Learning course by Prof. Alexander Amini and Prof.Ava Amini. The course started at the end of April and will run until June. The course lectures are published weekly. The course syllabus keeps changing from year to year, reflecting the rapid changes in this field.

ramikrispin,
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(2/2) The course covers the following topics:
✅ Deep learning foundation
✅ Computer vision
✅ Deep generative modeling
✅ Reinforcement learning
✅ Robot learning
✅ Text to image

Resources 📚
Course website 🔗: http://introtodeeplearning.com/
Video lectures 📽️: https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

ramikrispin, to random
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(2/2) I am mainly interested in the interaction between Rust and WebAssembly, and MLOps applications.

Here is a great lecture by Lincoln Colling from the University of Sussex about the data science applications of Rust 👇🏼

📽️ https://www.youtube.com/watch?v=E_je8_5WeDk

ramikrispin, to rust
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(1/2) I recently posted a few posts about Rust 🦀 and my intention to leverage it for data science applications. Multiple people asked if Rust is a substitute for R or Python, and the short answer (in my opinion) is no. I see Rust as a complementary or supporting language that could make languages like R and Python faster.

Polaris 🐻‍❄️ is one example of a Python 🐍 application that uses Rust on the backend. 🧵👇🏼

ramikrispin, to random
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(2/2) That includes topics such as:
✅ Probabilistic and learning algorithms for Deep generative models
✅ Variational autoencoders
✅ Generative adversarial networks and autoregressive models
✅ Normalizing flow models
✅ Energy-based models and score-based models

Lectures 📽️: https://www.youtube.com/playlist?list=PLoROMvodv4rPOWA-omMM6STXaWW4FvJT8
Course website 🔗: https://deepgenerativemodels.github.io/

ramikrispin, to datascience
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(1/2) Happy Tuesday! ☀️

Deep Generative Models - New Stanford Course 🚀👇🏼

Stanford University released a new course last week focusing on Deep Generative Models. The course, by Prof. Stefano Ermon, focuses on the models beyond GenAI models.

ramikrispin, to OpenAI
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The new OpenAI model is out - GPT 4 Omni, supporting video, audio, and vision 🤯

https://openai.com/index/hello-gpt-4o/

#openai #datascience #llm #deeplearning #genai

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