Multi-label classification involves predicting zero or more class labels. Unlike normal classification tasks where class labels are mutually exclusive, multi-label classification requires specialized machine learning algorithms that support predicting multiple mutually non-exclusive classes or “labels.” Deep learning neural networks are an example of an algorithm that natively supports multi-label classification problems. Neural network models for […]
Multi-output regression involves predicting two or more numerical variables. Unlike normal regression where a single value is predicted for each sample, multi-output regression requires specialized machine learning algorithms that support outputting multiple variables for each prediction. Deep learning neural networks are an example of an algorithm that natively supports multi-output regression problems. Neural network models […]
Supervised learning in machine learning can be described in terms of function approximation. Given a dataset comprised of inputs and outputs, we assume that there is an unknown underlying function that is consistent in mapping inputs to outputs in the target domain and resulted in the dataset. We then use supervised learning algorithms to approximate […]
Predictive modeling with deep learning is a skill that modern developers need to know. TensorFlow is the premier open-source deep learning framework developed and maintained by Google. Although using TensorFlow directly can be challenging, the modern tf.keras API brings Keras’s simplicity and ease of use to the TensorFlow project. Using tf.keras allows you to design, […]
Machine learning and deep learning models, like those in Keras, require all input and output variables to be numeric. This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model. The two most popular techniques are an integer encoding and a one hot […]
A lot is happening in the world of AI at the moment. Some of you may be wondering how machines have the ability to do what they can do. How can they recognise images, understand speech, and even reply to my requests??? Welcome to the world of Deep Learning. Deep Learning is a subfield of […]
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? […]
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 […]
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 […]
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 […]
