Launch and view Tensorboards in VS Code —
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Updated
Jun 12, 2024 - TypeScript
Launch and view Tensorboards in VS Code —
⚡️SwanLab: your ML experiment notebook. 你的AI实验笔记本,跟踪与可视化你的机器学习全流程
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
Comparing the performance of MLP (multilayer perceptron) and CNN (convolutional neural network) on USPS dataset and visualizing it via TensorBoard.
A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
Experiment tracking server focused on speed and scalability
Reduce multiple PyTorch TensorBoard runs to new event (or CSV) files.
An exploration of the mechanics of transfer learning in TensorFlow
C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
A docker-based starter kit for machine learning via jupyter notebooks. Designed for those who just want a runtime environment and get on with machine learning. Docker Hub:
Study and implementation about deep learning models, architectures, applications and frameworks
Pneumo.ai is a Streamlit web app for detecting pneumonia from X-rays, leveraging CNN variants like AlexNet, VGG, and ResNet18, with ResNet18 leading with an accuracy of 0.89. It employs early stopping callbacks for efficient model training, visualizes training using TensorBoard, and optimizes inference time with pruning and quantization techniques.
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
Machine Learning Operator & Controller for Kubernetes
A multitask neural network that does both regression and classification
This repository allows you to get started with a gui based training a State-of-the-art Deep Learning model with little to no configuration needed! NoCode training with TensorFlow has never been so easy.
Code for Tensorflow Machine Learning Cookbook
A deep learning model to age faces in the wild, currently runs at 60+ fps on GPUs
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