Francois is not only the creator of the Keras deep learning library, but he's also a Google AI researcher. He will also be speaking at PyImageConf 2018 in August of this year. Please, join me in welcoming Francois to the PyImageSearch -- it is truthfully a privilege to have him here. I know you are very busy with your work at Google AI and on the Keras library -- I really appreciate you taking the time to do this interview. It's quite the honor to have you on the PyImageSearch blog! For people who don't know you, who are you and what do you do?

Exploding gradients are a problem where large error gradients accumulate and result in very large updates to neural network model weights during training. This has the effect of your model being unstable and unable to learn from your training data. In this post, you will discover the problem of exploding gradients with deep artificial neural networks. A Gentle Introduction to Exploding Gradients in Recurrent Neural Networks Photo by Taro Taylor, some rights reserved. An error gradient is the direction and magnitude calculated during the training of a neural network that is used to update the network weights in the right direction and by the right amount.

Building a model in Keras starts by constructing an empty Sequential model. The result of Sequential, as with most of the functions provided by kerasR, is a python.builtin.object. This object type, defined from the reticulate package, provides direct access to all of the methods and attributes exposed by the underlying python class. To access these, we use the $ operator followed by the method name. Layers are added by calling the method add.

Time Series prediction is a difficult problem both to frame and to address with machine learning. In this post you will discover how to develop neural network models for time series prediction in Python using the Keras deep learning library. The problem we are going to look at in this post is the international airline passengers prediction problem. This is a problem where given a year and a month, the task is to predict the number of international airline passengers in units of 1,000. Below is a sample of the first few lines of the file.

Time Series prediction is a difficult problem both to frame and to address with machine learning. In this post you will discover how to develop neural network models for time series prediction in Python using the Keras deep learning library. The problem we are going to look at in this post is the international airline passengers prediction problem. This is a problem where given a year and a month, the task is to predict the number of international airline passengers in units of 1,000. Below is a sample of the first few lines of the file.

Keras is a Python library for deep learning that wraps the efficient numerical libraries TensorFlow and Theano. Keras allows you to quickly and simply design and train neural network and deep learning models. In this post you will discover how to effectively use the Keras library in your machine learning project by working through a binary classification project step-by-step. Binary Classification Worked Example with the Keras Deep Learning Library Photo by Mattia Merlo, some rights reserved. The dataset we will use in this tutorial is the Sonar dataset.

Keras is a deep learning library that wraps the efficient numerical libraries Theano and TensorFlow. In this post you will discover how to develop and evaluate neural network models using Keras for a regression problem. Regression Tutorial with Keras Deep Learning Library in Python Photo by Salim Fadhley, some rights reserved. The problem that we will look at in this tutorial is the Boston house price dataset. You can download this dataset and save it to your current working directly with the file name housing.csv.

Keras is a Python library for deep learning that wraps the efficient numerical libraries Theano and TensorFlow. In this post you will discover how you can use Keras to develop and evaluate neural network models for multi-class classification problems. Multi-Class Classification Tutorial with the Keras Deep Learning Library Photo by houroumono, some rights reserved. In this tutorial we will use the standard machine learning problem called the iris flowers dataset. This dataset is well studied and is a good problem for practicing on neural networks because all of the 4 input variables are numeric and have the same scale in centimeters.