aylazai / Stock-Trend-PredictionLinks
This project is a stock trend prediction web application created using Python and Streamlit. The purpose of this web application is to allow users to input stocks they wish to predict and view visualizations of the predicted versus original values.
☆10Updated 2 years ago
Alternatives and similar repositories for Stock-Trend-Prediction
Users that are interested in Stock-Trend-Prediction are comparing it to the libraries listed below
Sorting:
- CUDA Templates for Linear Algebra Subroutines☆8,527Updated 2 weeks ago
- Puzzles for learning Triton☆2,022Updated 10 months ago
- GPU programming related news and material links☆1,717Updated 3 weeks ago
- Tile primitives for speedy kernels☆2,767Updated 2 weeks ago
- Training materials associated with NVIDIA's CUDA Training Series (www.olcf.ornl.gov/cuda-training-series/)☆872Updated last year
- Material for gpu-mode lectures☆5,143Updated 2 weeks ago
- Fast CUDA matrix multiplication from scratch☆886Updated last month
- Solve puzzles. Improve your pytorch.☆3,727Updated last year
- CUDA Library Samples☆2,114Updated this week
- Code from the "CUDA Crash Course" YouTube series by CoffeeBeforeArch☆875Updated 2 years ago
- ☆1,480Updated 3 months ago
- CUDA Core Compute Libraries☆1,950Updated this week
- Samples for CUDA Developers which demonstrates features in CUDA Toolkit☆8,213Updated last month
- how to optimize some algorithm in cuda.☆2,537Updated last week
- A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper, Ada and Bla…☆2,763Updated this week
- This is a series of GPU optimization topics. Here we will introduce how to optimize the CUDA kernel in detail. I will introduce several…☆1,160Updated 2 years ago
- FlashInfer: Kernel Library for LLM Serving☆3,861Updated this week
- CUDA Learning guide☆450Updated last year
- Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels☆3,197Updated this week
- Learn CUDA Programming, published by Packt☆1,196Updated last year
- GPGPU-Sim provides a detailed simulation model of contemporary NVIDIA GPUs running CUDA and/or OpenCL workloads. It includes support for…☆1,447Updated 7 months ago
- An ML Systems Onboarding list☆907Updated 8 months ago
- Flash Attention in ~100 lines of CUDA (forward pass only)☆940Updated 9 months ago
- TT-NN operator library, and TT-Metalium low level kernel programming model.☆1,222Updated this week
- CUDA Python: Performance meets Productivity☆2,988Updated this week
- Mirage Persistent Kernel: Compiling LLMs into a MegaKernel☆1,866Updated this week
- 🚀 Efficient implementations of state-of-the-art linear attention models☆3,431Updated last week
- 📚LeetCUDA: Modern CUDA Learn Notes with PyTorch for Beginners🐑, 200+ CUDA Kernels, Tensor Cores, HGEMM, FA-2 MMA.🎉☆7,874Updated 3 weeks ago
- A list of awesome compiler projects and papers for tensor computation and deep learning.☆2,657Updated 11 months ago
- ☆183Updated last year