The Keras Python library makes creating deep learning models fast and easy. The sequential API allows you to create models layer-by-layer for most problems. It is limited in that it does not allow you to create models that share layers or have multiple inputs or outputs. The functional API in Keras is an alternate way […]

# Archive | Deep Learning

## How to Use Metrics for Deep Learning with Keras in Python

The Keras library provides a way to calculate and report on a suite of standard metrics when training deep learning models. In addition to offering standard metrics for classification and regression problems, Keras also allows you to define and report on your own custom metrics when training deep learning models. This is particularly useful if […]

## 10 Command Line Recipes for Deep Learning on Amazon Web Services

Running large deep learning processes on Amazon Web Services EC2 is a cheap and effective way to learn and develop models. For just a few dollars you can get access to tens of gigabytes of RAM, tens of CPU cores, and multiple GPUs. I highly recommend it. If you are new to EC2 or the […]

## 9 Ways to Get Help with Deep Learning in Keras

Keras is a Python deep learning library that can use the efficient Theano or TensorFlow symbolic math libraries as a backend. Keras is so easy to use that you can develop your first Multilayer Perceptron, Convolutional Neural Network, or LSTM Recurrent Neural Network in minutes. You may have technical questions when you get started using […]

## A Gentle Introduction to Mini-Batch Gradient Descent and How to Configure Batch Size

Stochastic gradient descent is the dominant method used to train deep learning models. There are three main variants of gradient descent and it can be confusing which one to use. In this post, you will discover the one type of gradient descent you should use in general and how to configure it. After completing this […]

## How to Get Reproducible Results with Keras

Neural network algorithms are stochastic. This means they make use of randomness, such as initializing to random weights, and in turn the same network trained on the same data can produce different results. This can be confusing to beginners as the algorithm appears unstable, and in fact they are by design. The random initialization allows […]

## How to Evaluate the Skill of Deep Learning Models

I often see practitioners expressing confusion about how to evaluate a deep learning model. This is often obvious from questions like: What random seed should I use? Do I need a random seed? Why don’t I get the same results on subsequent runs? In this post, you will discover the procedure that you can use […]

## What You Know About Deep Learning Is A Lie

Getting started in deep learning is a struggle. It’s a struggle because deep learning is taught by academics, for academics. If you’re a developer (or practitioner), you’re different. You want results. The way practitioners learn new technologies is by developing prototypes that deliver value quickly. This is a top-down approach to learning, but it is not the way […]

## What is Deep Learning?

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. If you are just starting out in the field of deep learning or you had some experience with neural networks some time ago, you may be confused. I know I was confused […]

## 5 Step Life-Cycle for Neural Network Models in Keras

Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle. In this post you will discover the step-by-step life-cycle for creating, training and evaluating deep learning neural networks in Keras and how to make predictions with a trained model. After reading this […]