Last Updated on August 27, 2020 Hyperparameter optimization is a big part of deep learning. The reason is that neural networks are notoriously difficult to configure and there are a lot of parameters that need to be set. On top of that, individual models can be very slow to train. In this post you will discover how you can use the grid search capability from the scikit-learn python machine learning library to tune the hyperparameters of Keras deep learning models. After […]

Last Updated on August 27, 2020 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 post you will know: How to define, compile, fit and evaluate a deep learning neural network in Keras. How […]

Last Updated on August 19, 2019 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 that deep learning is taught. There is another way. A way that works for top-down practitioners like you. In this […]

Last Updated on August 14, 2020 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 to evaluate deep learning models and the rationale for using it. You will also discover useful related statistics that […]

Last Updated on August 19, 2019 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 the network to learn a good approximation for the function being learned. Nevertheless, there are times when you need […]

Last Updated on August 19, 2019 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 post, you will know: What gradient descent is and how it works from a high level. What batch, stochastic, […]

Last Updated on August 19, 2019 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 Keras. You may need a little help. In this post, you will discover 9 places where you can ask […]

Last Updated on August 19, 2019 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 Linux command line, there are a suite of commands that you will find invaluable when running your deep learning […]

Last Updated on August 27, 2020 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 you want to keep track of a performance measure that better captures the skill of your model during training. […]

Last Updated on May 28, 2020 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 of creating models that offers a lot more flexibility, including creating more complex models. In this tutorial, you will […]