Python Type Hints: How to Gradually Add Types for Third Party Packages
Hynek Schlawack recently described graduality as Python’s super power: the ability to prototype in ...
https://adamj.eu/tech/2022/08/23/python-type-hints-gradually-add-types-for-third-party-packages/
Hynek Schlawack recently described graduality as Python’s super power: the ability to prototype in ...
https://adamj.eu/tech/2022/08/23/python-type-hints-gradually-add-types-for-third-party-packages/
adamj.eu
Python Type Hints: How to Gradually Add Types for Third Party Packages - Adam Johnson
Hynek Schlawack recently described graduality as Python’s super power: the ability to prototype in the REPL, and gradually add linting, type checking, and other practices to refine your code into maintainable, production-ready software. You can also apply…
SymPy: Solving Math Equations in Python and Jupyter
https://codesolid.com/sympy-solving-math-equations-in-python/
https://codesolid.com/sympy-solving-math-equations-in-python/
Python library for univariate regression, interpolation, and smoothing
https://github.com/brendanartley/Regressio
https://github.com/brendanartley/Regressio
GitHub
GitHub - brendanartley/Regressio: A python library for univariate regression, interpolation, and smoothing.
A python library for univariate regression, interpolation, and smoothing. - brendanartley/Regressio
DevCase
A privacy-focused and secure CMS, Blog and Portfolio made with Python & Django. Designed with developers and IT professionals in mind.
https://github.com/rob32/dev-case
A privacy-focused and secure CMS, Blog and Portfolio made with Python & Django. Designed with developers and IT professionals in mind.
https://github.com/rob32/dev-case
GitHub
GitHub - rob32/dev-case: A privacy-focused and secure CMS, Blog and Portfolio made with Python & Django. Designed with developers…
A privacy-focused and secure CMS, Blog and Portfolio made with Python & Django. Designed with developers and IT professionals in mind. - rob32/dev-case
Bayesian Age/Period/Cohort Models in Python with PyMC
This post shows how to use pymc to build Bayesian APC models in Python and presents a series of increasingly sophistocated systems of priors to resolve the inferential challenges these models pose.
https://austinrochford.com/posts/apc-pymc.html
This post shows how to use pymc to build Bayesian APC models in Python and presents a series of increasingly sophistocated systems of priors to resolve the inferential challenges these models pose.
https://austinrochford.com/posts/apc-pymc.html
Austin Rochford
Bayesian Age/Period/Cohort Models in Python with PyMC
For my day job, I spend a lot of time thinking about e-commerce analytics and cohort analysis in particular. Statistical age-period-cohort (APC) models are important in many fields such as epidemiolo
The Jupyter+git problem is now solved
Jupyter notebooks don’t work with git by default. With nbdev2, the Jupyter+git problem has been totally solved. It provides a set of hooks which provide clean git diffs, solve most git conflicts automatically, and ensure that any remaining conflicts can be resolved entirely within the standard Jupyter notebook environment. To
https://www.fast.ai/2022/08/25/jupyter-git/
Jupyter notebooks don’t work with git by default. With nbdev2, the Jupyter+git problem has been totally solved. It provides a set of hooks which provide clean git diffs, solve most git conflicts automatically, and ensure that any remaining conflicts can be resolved entirely within the standard Jupyter notebook environment. To
https://www.fast.ai/2022/08/25/jupyter-git/
pydantic / pydantic
Data parsing and validation using Python type hints
https://github.com/pydantic/pydantic
Data parsing and validation using Python type hints
https://github.com/pydantic/pydantic
GitHub
GitHub - pydantic/pydantic: Data validation using Python type hints
Data validation using Python type hints. Contribute to pydantic/pydantic development by creating an account on GitHub.
5 Tips To Achieve Low Coupling In Your Python Code
In this video I share 5 tips to help you write code that has low coupling. I'll show you several examples and also share a story of a technique I used several times in the past that has really helped me reduce coupling and solve more complex software design problems.
https://www.youtube.com/watch?v=qR4-PBLUZNw
In this video I share 5 tips to help you write code that has low coupling. I'll show you several examples and also share a story of a technique I used several times in the past that has really helped me reduce coupling and solve more complex software design problems.
https://www.youtube.com/watch?v=qR4-PBLUZNw
YouTube
5 Tips To Achieve Low Coupling In Your Python Code
👷 Review code better and faster with my 3-Factor Framework: https://arjan.codes/diagnosis.
In this video I share 5 tips to help you write code that has low coupling. I'll show you several examples and also share a story of a technique I used several times…
In this video I share 5 tips to help you write code that has low coupling. I'll show you several examples and also share a story of a technique I used several times…
Building a backend from scratch using only OpenAI Codex
Developing with Codex is a bit special, and it sometimes takes a few attempts to get it to write exactly what you want it to. But in broad strokes, getting from nothing to something in just 10 prompts is really impressive and encouraging.
https://codeball.ai/blog/codex-todo-mvc
Developing with Codex is a bit special, and it sometimes takes a few attempts to get it to write exactly what you want it to. But in broad strokes, getting from nothing to something in just 10 prompts is really impressive and encouraging.
https://codeball.ai/blog/codex-todo-mvc
Cog
Cog is an open-source tool that lets you package machine learning models in a standard, production-ready container.
https://github.com/replicate/cog
Cog is an open-source tool that lets you package machine learning models in a standard, production-ready container.
https://github.com/replicate/cog
GitHub
GitHub - replicate/cog: Containers for machine learning
Containers for machine learning. Contribute to replicate/cog development by creating an account on GitHub.
Stable Diffusion with Diffusers
In this post, we want to show how to use Stable Diffusion with the Diffusers library, explain how the model works and finally dive a bit deeper into how diffusers allows one to customize the image generation pipeline.
https://huggingface.co/blog/stable_diffusion
In this post, we want to show how to use Stable Diffusion with the Diffusers library, explain how the model works and finally dive a bit deeper into how diffusers allows one to customize the image generation pipeline.
https://huggingface.co/blog/stable_diffusion
huggingface.co
Stable Diffusion with 🧨 Diffusers
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
facebookresearch / esm
Evolutionary Scale Modeling (esm): Pretrained language models for proteins
https://github.com/facebookresearch/esm
Evolutionary Scale Modeling (esm): Pretrained language models for proteins
https://github.com/facebookresearch/esm
stable-diffusion
Stable Diffusion is a latent text-to-image diffusion model. Similar to Google's Imagen, this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
https://github.com/CompVis/stable-diffusion
Stable Diffusion is a latent text-to-image diffusion model. Similar to Google's Imagen, this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
https://github.com/CompVis/stable-diffusion
GitHub
GitHub - CompVis/stable-diffusion: A latent text-to-image diffusion model
A latent text-to-image diffusion model. Contribute to CompVis/stable-diffusion development by creating an account on GitHub.
Indoor Asset Tracking using Wi-Fi Triangulation
Build a complete indoor asset tracking IoT solution using the Blues Wireless Notecard, an ESP32 host MCU, the Notecarrier-F, and Datacake.
https://www.hackster.io/rob-lauer/indoor-asset-tracking-using-wi-fi-triangulation-5c5963
Build a complete indoor asset tracking IoT solution using the Blues Wireless Notecard, an ESP32 host MCU, the Notecarrier-F, and Datacake.
https://www.hackster.io/rob-lauer/indoor-asset-tracking-using-wi-fi-triangulation-5c5963
Hackster.io
Indoor Asset Tracking using Wi-Fi Triangulation
Build a complete indoor asset tracking IoT solution using the Blues Wireless Notecard, an ESP32 host MCU, the Notecarrier-F, and Datacake.
VNext
Next-generation Video instance recognition framework on top of Detectron2 which supports SeqFormer(ECCV Oral) and IDOL(ECCV Oral)).
https://github.com/wjf5203/VNext
Next-generation Video instance recognition framework on top of Detectron2 which supports SeqFormer(ECCV Oral) and IDOL(ECCV Oral)).
https://github.com/wjf5203/VNext
GitHub
GitHub - wjf5203/VNext: Next-generation Video instance recognition framework on top of Detectron2 which supports InstMove (CVPR…
Next-generation Video instance recognition framework on top of Detectron2 which supports InstMove (CVPR 2023), SeqFormer(ECCV Oral), and IDOL(ECCV Oral)) - wjf5203/VNext
Python in Visual Studio Code – September 2022 Release
This release includes the following announcements:
https://devblogs.microsoft.com/python/pvsc-sept-2022/
This release includes the following announcements:
https://devblogs.microsoft.com/python/pvsc-sept-2022/
Python
Python in Visual Studio Code - September 2022 Release
The September 2022 release of the Python and Jupyter extensions for Visual Studio Code are now available. This release includes improved IntelliSense support for Jupyter Notebooks, a new Flake8 extension and internship highlights. Keep on reading to learn…
Accelerate Python code 100x by import taichi as ti
There is no universal solution to all optimization problems. That's partially why Python is fascinating. You can always find/create an easy-to-use tool that can precisely solve your problem at hand. In terms of scientific computing, Taichi is an ideal option within Python that can help you achieve performance comparable to C/C++.
https://docs.taichi-lang.org/blog/accelerate-python-code-100x
There is no universal solution to all optimization problems. That's partially why Python is fascinating. You can always find/create an easy-to-use tool that can precisely solve your problem at hand. In terms of scientific computing, Taichi is an ideal option within Python that can help you achieve performance comparable to C/C++.
https://docs.taichi-lang.org/blog/accelerate-python-code-100x
docs.taichi-lang.org
Accelerate Python code 100x by import taichi as ti | Taichi Docs
Python has become the most popular language in many rapidly evolving sectors, such as deep learning and data sciences. Yet its easy readability comes at the cost of performance. Of course, we all complain about program performance from time to time, and Python…