TensorFlow-Examples
TensorFlow-Tutorials
| TensorFlow-Examples | TensorFlow-Tutorials | |
|---|---|---|
| 3 | 2 | |
| 43,447 | 9,272 | |
| - | - | |
| 0.0 | 0.0 | |
| 5 months ago | almost 4 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 or later | MIT License |
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TensorFlow-Examples
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Top Github repositories for 10+ programming languages
Tensorflow examples
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Keras vs. TensorFlow
A linear regression model
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Tensorman and RTX 30-Series GPU's
When I run this simple project, the log output is below. There is a 5-minute pause at 16:48. There is a second pause at the end of the script before the output of the example (final output excluded). This project runs quickly if I exclude "--gpu" and run it on the CPU.
TensorFlow-Tutorials
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Probabilistic forecasting
"deep neural network" https://github.com/Hvass-Labs/TensorFlow-Tutorials
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Plagiarism is just bad
The majority of this code is taken from the TensorFlow-Tutorials. I highly recommend them to those who want to get started with TensorFlow.
What are some alternatives?
lego-mindstorms - My LEGO MINDSTORMS projects (using set 51515 electronics)
car-damage-detection - Detectron2 for car damage detection using custom dataset
graphkit-learn - A python package for graph kernels, graph edit distances, and graph pre-image problem.
YOLO_Object_Detection - This is the code for "YOLO Object Detection" by Siraj Raval on Youtube
pyVHR - Python framework for Virtual Heart Rate
Practical_RL - A course in reinforcement learning in the wild
rmi - A learned index structure
TensorFlow2.0_Notebooks - Implementation of a series of Neural Network architectures in TensorFow 2.0
Deep-Learning-With-TensorFlow-Blog-series - All the resources and hands-on exercises for you to get started with Deep Learning in TensorFlow [Moved to: https://github.com/Rishit-dagli/Deep-Learning-With-TensorFlow]
Reinforcement-Learning-2nd-Edition-by-Sutton-Exercise-Solutions - Solutions of Reinforcement Learning, An Introduction
Deep-Learning-Hardware-Benchmark - This repository contains the proposed implementation for benchmarking in order to evaluate whether a setup of hardware is feasible for deep learning projects.
Deep-Learning-In-Production - Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.