Resources for developers using Python for scientific computing and quantitative analysis
Training is without a doubt the most important part of developing a machine learning application. It’s when you start realizing whether or not your model is worth it, how your hyperparameters should look like and what do you need to change in your architecture. In general, most machine learning engineers spend quite some time on training, experimenting with different models, tuning their architecture and discovering the best metrics and losses for their problem.
TensorFlow Lite is a framework for running lightweight machine learning models, and it’s perfect for low-power devices like the Raspberry Pi! This video shows how to set up TensorFlow Lite on the Raspberry Pi for running object detection models to locate and identify objects in real-time webcam feeds, videos, or images.
This document serves as an introduction, crash course, and quick API reference for TensorFlow 2.0.
TensorFlow 2 is now live! This tutorial walks you through the process of building a simple CIFAR-10 image classifier using deep learning. In this tutorial, we will:
The code in this tutorial is available here.
Advanced machine learning everyone can use. Stage data. Build models with no code. Manage models in production.
One of the most common utilizations of TensorFlow and Keras is the recognition/classification of images. If you want to learn how to use Keras to classify or recognize images, this article will teach you how.
A tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.
