n this tutorial, you will learn how to detect fire and smoke using Computer Vision, OpenCV, and the Keras Deep Learning library.
In this tutorial, you will learn how to train your own traffic sign classifier/recognizer capable of obtaining over 95% accuracy using Keras and Deep Learning.
This document serves as an introduction, crash course, and quick API reference for TensorFlow 2.0.
In this tutorial, you will learn how to automatically find learning rates using Keras. This guide provides a Keras implementation of fast.ai’s popular “lr_find” method.
In this tutorial, you will learn how to use Cyclical Learning Rates (CLR) and Keras to train your own neural networks. Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model.
In today’s tutorial, you will learn how to use Keras’ ImageDataGenerator class to perform data augmentation. I’ll also dispel common confusions surrounding what data augmentation is, why we use data augmentation, and what it does/does not do.
In this tutorial, you will learn how to use Keras and Mask R-CNN to perform instance segmentation (both with and without a GPU).
Using Mask R-CNN we can perform both: Object detection, giving us the (x, y)-bounding box coordinates of for each object in an image; Instance segmentation, enabling us to obtain a pixel-wise mask for each individual object in an image.
In this tutorial, you will learn how to perform fine-tuning with Keras and Deep Learning.
We will take a CNN pre-trained on the ImageNet dataset and fine-tune it to perform image classification and recognize classes it was never trained on.
Today is the final post in our three-part series on fine-tuning:
- Part #1: Transfer learning with Keras and Deep Learning
- Part #2: Feature extraction with on large datasets with Keras and Deep Learning
- Part #3: Fine-tuning with Keras and Deep Learning (today’s post)