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This is a guest post by Adrian Rosebrock. Adrian is the author of PyImageSearch.com, a blog about computer vision and deep learning. Adrian recently finished authoring Deep Learning for Computer Vision with Python, a new book on deep learning for computer vision and image recognition using Keras. In this tutorial, we will present a simple method to take a Keras model and deploy it as a REST API. T
Writing code is rarely just a private affair between you and your computer. Code is not just meant for machines; it has human users. It is meant to be read by people, used by other developers, maintained and built upon. Developers who produce better code, in greater quantity, when they are kept happy and productive, working with tools they love. Developers who unfortunately are often being let dow
Note: this post is from 2017. See this tutorial for an up-to-date version of the code used here. I see this question a lot -- how to implement RNN sequence-to-sequence learning in Keras? Here is a short introduction. Note that this post assumes that you already have some experience with recurrent networks and Keras. What is sequence-to-sequence learning? Sequence-to-sequence learning (Seq2Seq) is
This post is adapted from Section 3 of Chapter 9 of my book, Deep Learning with Python (Manning Publications). It is part of a series of two posts on the current limitations of deep learning, and its future. You can read the first part here: The Limitations of Deep Learning. Given what we know of how deep nets work, of their limitations, and of the current state of the research landscape, can we p
This post is adapted from Section 2 of Chapter 9 of my book, Deep Learning with Python (Manning Publications). It is part of a series of two posts on the current limitations of deep learning, and its future. This post is targeted at people who already have significant experience with deep learning (e.g. people who have read chapters 1 through 8 of the book). We assume a lot of pre-existing knowled
This is a step by step guide to start running deep learning Jupyter notebooks on an AWS GPU instance, while editing the notebooks from anywhere, in your browser. This is the perfect setup for deep learning research if you do not have a GPU on your local machine. What are Jupyter notebooks? Why run Jupyter notebooks on AWS GPUs? A Jupyter notebook is a web app that allows you to write and annotate
Keras was released two years ago, in March 2015. It then proceeded to grow from one user to one hundred thousand. Hundreds of people have contributed to the Keras codebase. Many thousands have contributed to the community. Keras has enabled new startups, made researchers more productive, simplified the workflows of engineers at large companies, and opened up deep learning to thousands of people wi
Note: this post was originally written in July 2016. It is now mostly outdated. Please see this example of how to use pretrained word embeddings for an up-to-date alternative. In this tutorial, we will walk you through the process of solving a text classification problem using pre-trained word embeddings and a convolutional neural network. The full code for this tutorial is available on Github. Wh
We all know about the incredible progress that deep learning has made in recent years. In just 5 years, we went from near-unusable speech recognition and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to beating a world champion. We went further than anybody could have foreseen --if you went back to 2010 and told AI researchers about the th
Note: this post was originally written in June 2016. It is now very outdated. Please see this guide to fine-tuning for an up-to-date alternative, or check out chapter 8 of my book "Deep Learning with Python (2nd edition)". In this tutorial, we will present a few simple yet effective methods that you can use to build a powerful image classifier, using only very few training examples --just a few hu
In this tutorial, we will answer some common questions about autoencoders, and we will cover code examples of the following models: a simple autoencoder based on a fully-connected layer a sparse autoencoder a deep fully-connected autoencoder a deep convolutional autoencoder an image denoising model a sequence-to-sequence autoencoder a variational autoencoder Note: all code examples have been updat
Note: this post is from April 2016. It no longer reflects TensorFlow and Keras best practices. Keras has now been integrated into TensorFlow. Please see the keras.io documentation for details. A complete guide to using Keras as part of a TensorFlow workflow If TensorFlow is your primary framework, and you are looking for a simple & high-level model definition interface to make your life easier, th
Keras was initially released a year ago, late March 2015. It has made tremendous progress since, both on the development front, and as a community. But continuous improvement isn't enough. A year of developing Keras, using Keras, and getting feedback from thousands of users has taught us a lot. To the point that we are now able to redesign it better than we could have the first time around. And so
Note: this post was originally written in January 2016. It is now very outdated. Please see this example of how to visualize convnet filters for an up-to-date alternative, or check out chapter 9 of my book "Deep Learning with Python (2nd edition)". An exploration of convnet filters with Keras In this post, we take a look at what deep convolutional neural networks (convnets) really learn, and how t
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