Pytorch
Pandas
Pytorch | Pandas | |
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349 | 400 | |
79,328 | 42,339 | |
1.7% | 0.8% | |
10.0 | 10.0 | |
5 days ago | 6 days ago | |
Python | Python | |
BSD 1-Clause License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Pytorch
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Mathematics secret behind AI on Digit Recognition
Hi everyone! I’m devloker, and today I’m excited to share a project I’ve been working on: a digit recognition system implemented using pure math functions in Python. This project aims to help beginners grasp the mathematics behind AI and digit recognition without relying on high-level libraries like TensorFlow or PyTorch. You can find the complete code on my GitHub repository.
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Top 17 Fast-Growing Github Repo of 2024
PyTorch
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AMD's MI300X Outperforms Nvidia's H100 for LLM Inference
> their own custom stack to interact with GPUs
lol completely made up.
are you conflating CUDA the platform with the C/C++ like language that people write into files that end with .cu? because while some people are indeed not writing .cu files, absolutely no one is skipping the rest of the "stack".
source: i work at one of these "mega corps". hell if you don't believe me go look at how many CUDA kernels pytorch has https://github.com/pytorch/pytorch/tree/main/aten/src/ATen/n....
> Everybody thinks it’s CUDA that makes Nvidia the dominant player.
it 100% does
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Awesome List
PyTorch - An open source machine learning framework. PyTorch Tutorials - Tutorials and documentation.
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Understanding GPT: How To Implement a Simple GPT Model with PyTorch
In this guide, we provided a comprehensive, step-by-step explanation of how to implement a simple GPT (Generative Pre-trained Transformer) model using PyTorch. We walked through the process of creating a custom dataset, building the GPT model, training it, and generating text. This hands-on implementation demonstrates the fundamental concepts behind the GPT architecture and serves as a foundation for more complex applications. By following this guide, you now have a basic understanding of how to create, train, and utilize a simple GPT model. This knowledge equips you to experiment with different configurations, larger datasets, and additional techniques to enhance the model's performance and capabilities. The principles and techniques covered here will help you apply transformer models to various NLP tasks, unlocking the potential of deep learning in natural language understanding and generation. The methodologies presented align with the advancements in transformer models introduced by Vaswani et al. (2017), emphasizing the power of self-attention mechanisms in processing sequences of data more effectively than traditional approaches (Vaswani et al., 2017). This understanding opens pathways to explore and innovate in the field of natural language processing using cutting-edge deep learning techniques (Kingma & Ba, 2015).
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Building a Simple Chatbot using GPT model - part 2
PyTorch is a powerful and flexible deep learning framework that offers a rich set of features for building and training neural networks.
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Clusters Are Cattle Until You Deploy Ingress
Oddly enough, sometimes, the best way to learn is by putting forth incorrect opinions or questions. Recently, while wrestling with AI project complexities, I pondered aloud whether all Docker images with AI models would inevitably be bulky due to PyTorch dependencies. To my surprise, this sparked many helpful responses, offering insights into optimizing image sizes. Being willing to be wrong opens up avenues for rapid learning.
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Tinygrad 0.9.0
Tinygrad targets consumer hardware (to be precise, only Radeon 7900XTX and nothing else[1]), while ROCm does not actually provide good support for such hardware. For example, last release of hipBLASLt-6.1.1 library has deep integration with PyTorch[1], while working only on AMD Instinct hardware. And even for the professional hardware out there, the support period is ridiculous: AMD Instinct MI100 (2020) is not supported. Only 4 years and tens of thousands of dollars worth of hardware is going to the trash, yay!
And to be more precise, they still use some core libraries from ROCm stack[3], they just don't use all these fancy multi-gigabyte[4] hardware-limited rocBLAS/hipBLASlt/rocWMMA/rocRAND/etc. libraries.
[1] https://tinygrad.org/#tinybox
[2] https://github.com/pytorch/pytorch/issues/119081
[3] https://github.com/tinygrad/tinygrad/blob/v0.9.0/tinygrad/ru...
[4] https://repo.radeon.com/rocm/yum/6.1.1/main/
- PyTorch 2.3: User-Defined Triton Kernels, Tensor Parallelism in Distributed
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Clasificador de imágenes con una red neuronal convolucional (CNN)
PyTorch (https://pytorch.org/)
Pandas
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Awesome List
Pandas - A powerful data analysis and manipulation library for Python. Pandas Documentation - Official documentation.
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The ultimate guide to creating a secure Python package
It's also possible for you to give a package an alias by using the as keyword. For instance, you could use the pandas package as pd like this:
- The Birth of Parquet
- PDEP-13: The Pandas Logical Type System
- PHP Doesn't Suck Anymore
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AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.
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Pandas reset_index(): How To Reset Indexes in Pandas
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.
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Deploying a Serverless Dash App with AWS SAM and Lambda
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
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Help Us Build Our Roadmap – Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
[1]: https://github.com/pandas-dev/pandas/issues/53999
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
What are some alternatives?
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
tensorflow - An Open Source Machine Learning Framework for Everyone
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
flax - Flax is a neural network library for JAX that is designed for flexibility.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
Keras - Deep Learning for humans
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
pyexcel - Single API for reading, manipulating and writing data in csv, ods, xls, xlsx and xlsm files