Last Updated on August 19, 2019 Convolutional neural networks are now capable of outperforming humans on some computer vision tasks, such as classifying images. That is, given a photograph of an object, answer the question as to which of 1,000 specific objects the photograph shows. A competition-winning model for this task is the VGG model by researchers at Oxford. What is important about this model, besides its capability of classifying objects in photographs, is that the model weights are freely […]

Last Updated on September 11, 2019 The Keras Python deep learning library provides tools to visualize and better understand your neural network models. In this tutorial, you will discover exactly how to summarize and visualize your deep learning models in Keras. After completing this tutorial, you will know: How to create a textual summary of your deep learning model. How to create a graph plot of your deep learning model. Best practice tips when developing deep learning models in Keras. […]

Last Updated on August 19, 2019 After you write your code, you must run your deep learning experiments on large computers with lots of RAM, CPU, and GPU resources, often a Linux server in the cloud. Recently, I was asked the question: “How do you run your deep learning experiments?” This is a good nuts-and-bolts question that I love answering. In this post, you will discover the approach, commands, and scripts that I use to run deep learning experiments on […]

Last Updated on August 27, 2020 Once you choose and fit a final deep learning model in Keras, you can use it to make predictions on new data instances. There is some confusion amongst beginners about how exactly to do this. I often see questions such as: How do I make predictions with my model in Keras? In this tutorial, you will discover exactly how you can make classification and regression predictions with a finalized deep learning model with the […]

Last Updated on October 26, 2019 Stochastic gradient descent is a learning algorithm that has a number of hyperparameters. Two hyperparameters that often confuse beginners are the batch size and number of epochs. They are both integer values and seem to do the same thing. In this post, you will discover the difference between batches and epochs in stochastic gradient descent. After reading this post, you will know: Stochastic gradient descent is an iterative learning algorithm that uses a training […]

Last Updated on August 19, 2019 What neural network is appropriate for your predictive modeling problem? It can be difficult for a beginner to the field of deep learning to know what type of network to use. There are so many types of networks to choose from and new methods being published and discussed every day. To make things worse, most neural networks are flexible enough that they work (make a prediction) even when used with the wrong type of […]

Last Updated on March 26, 2020 The weights of artificial neural networks must be initialized to small random numbers. This is because this is an expectation of the stochastic optimization algorithm used to train the model, called stochastic gradient descent. To understand this approach to problem solving, you must first understand the role of nondeterministic and randomized algorithms as well as the need for stochastic optimization algorithms to harness randomness in their search process. In this post, you will discover […]

Last Updated on November 1, 2019 Practical deep learning is a challenging subject in which to get started. It is often taught in a bottom-up manner, requiring that you first get familiar with linear algebra, calculus, and mathematical optimization before eventually learning the neural network techniques. This can take years, and most of the background theory will not help you to get good results, fast. Instead, a top-down approach can be used where first you learn how to get results […]

Last Updated on August 19, 2019 Deep learning is not a magic bullet, but the techniques have shown to be highly effective in a large number of very challenging problem domains. This means that there is a ton of demand by businesses for effective deep learning practitioners. The problem is, how can the average business differentiate between good and bad practitioners? As a deep learning practitioner, how can you best demonstrate that you can deliver skillful deep learning models? In […]

Last Updated on August 27, 2020 Once you fit a deep learning neural network model, you must evaluate its performance on a test dataset. This is critical, as the reported performance allows you to both choose between candidate models and to communicate to stakeholders about how good the model is at solving the problem. The Keras deep learning API model is very limited in terms of the metrics that you can use to report the model performance. I am frequently […]