The promise of deep learning in the field of computer vision is better performance by models that may require more data but less digital signal processing expertise to train and operate. There is a lot of hype and large claims around deep learning methods, but beyond the hype, deep learning methods are achieving state-of-the-art results […]
Computer Vision, often abbreviated as CV, is defined as a field of study that seeks to develop techniques to help computers “see” and understand the content of digital images such as photographs and videos. The problem of computer vision appears simple because it is trivially solved by people, even very young children. Nevertheless, it largely […]
Computer vision is a subfield of artificial intelligence concerned with understanding the content of digital images, such as photographs and videos. Deep learning has made impressive inroads on challenging computer vision tasks and makes the promise of further advances. Before diving into the application of deep learning techniques to computer vision, it may be helpful […]
Computer vision is perhaps one area that has been most impacted by developments in deep learning. It can be difficult to both develop and to demonstrate competence with deep learning for problems in the field of computer vision. It is not clear how to get started, what the most important techniques are, and the types […]
Supervised machine learning is often described as the problem of approximating a target function that maps inputs to outputs. This description is characterized as searching through and evaluating candidate hypothesis from hypothesis spaces. The discussion of hypotheses in machine learning can be confusing for a beginner, especially when “hypothesis” has a distinct, but related meaning […]
Deep learning neural networks are challenging to configure and train. There are decades of tips and tricks spread across hundreds of research papers, source code, and in the heads of academics and practitioners. The book “Neural Networks: Tricks of the Trade” originally published in 1998 and updated in 2012 at the cusp of the deep […]
Machine learning and deep learning methods are often reported to be the key solution to all predictive modeling problems. An important recent study evaluated and compared the performance of many classical and modern machine learning and deep learning methods on a large and diverse set of more than 1,000 univariate time series forecasting problems. The […]
A foundation in statistics is required to be effective as a machine learning practitioner. The book “All of Statistics” was written specifically to provide a foundation in probability and statistics for computer science undergraduates that may have an interest in data mining and machine learning. As such, it is often recommended as a book to […]
The machine learning practitioner has a tradition of algorithms and a pragmatic focus on results and model skill above other concerns such as model interpretability. Statisticians work on much the same type of modeling problems under the names of applied statistics and statistical learning. Coming from a mathematical background, they have more of a focus […]
Systematic experimentation is a key part of applied machine learning. Given the complexity of machine learning methods, they resist formal analysis methods. Therefore, we must learn about the behavior of algorithms on our specific problems empirically. We do this using controlled experiments. In this tutorial, you will discover the important role that controlled experiments play […]
