If we compute the partial derivatives of the cross-entropy relatively to all the weights and all the biases we obtain a "gradient", computed for a given image, label, and present value of weights and biases. Remember that we can have millions of weights and biases so computing the gradient sounds like a lot of work. Fortunately, TensorFlow does it for us. The mathematical property of a gradient is that it points "up". Since we want to go where the cross-entropy is low, we go in the opposite direction.

In this tutorial, you will learn how to automatically detect natural disasters (earthquakes, floods, wildfires, cyclones/hurricanes) with up to 95% accuracy using Keras, Computer Vision, and Deep Learning. I remember the first time I ever experienced a natural disaster -- I was just a kid in kindergarten, no more than 6-7 years old. We were outside for recess, playing on the jungle gym, running around like the wild animals that young children are. Rain was in the forecast. My mother had given me a coat to wear outside, but I was hot and unconformable -- the humidity made the cotton/polyester blend stick to my skin.

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. Last weekend I drove down to Maryland to visit my parents. As I pulled into their driveway I noticed something strange -- there was a car I didn't recognize sitting in my dad's parking spot. I parked my car, grabbed my bags out of the trunk, and before I could even get through the front door, my dad came out, excited and enlivened, exclaiming that he had just gotten back from the car dealership and traded in his old car for a brand new 2020 Honda Accord. Most everyone enjoys getting a new car, but for my dad, who puts a lot of miles on his car each year for work, getting a new car is an especially big deal.

In this tutorial you will learn how to train a simple Convolutional Neural Network (CNN) with Keras on the Fashion MNIST dataset, enabling you to classify fashion images and categories. The Fashion MNIST dataset is meant to be a (slightly more challenging) drop-in replacement for the (less challenging) MNIST dataset. Throughout this tutorial, you will learn how to train a simple Convolutional Neural Network (CNN) with Keras on the Fashion MNIST dataset, giving you not only hands-on experience working with the Keras library but also your first taste of clothing/fashion classification. To learn how to train a Keras CNN on the Fashion MNIST dataset, just keep reading! In the first part of this tutorial, we will review the Fashion MNIST dataset, including how to download it to your system.