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 […]
The field of computer vision is shifting from statistical methods to deep learning neural network methods. There are still many challenging problems to solve in computer vision. Nevertheless, deep learning methods are achieving state-of-the-art results on some specific problems. It is not just the performance of deep learning models on benchmark problems that is most […]
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 […]
Skills in deep learning are in great demand, although these skills can be challenging to identify and to demonstrate. Explaining that you are familiar with a technique or type of problem is very different to being able to use it effectively with open source APIs on real datasets. Perhaps the most effective way of demonstrating […]
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? […]
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 […]
Or, Why Stochastic Gradient Descent Is Used to Train Neural Networks. Fitting a neural network involves using a training dataset to update the model weights to create a good mapping of inputs to outputs. This training process is solved using an optimization algorithm that searches through a space of possible values for the neural network […]
A learning curve is a plot of model learning performance over experience or time. Learning curves are a widely used diagnostic tool in machine learning for algorithms that learn from a training dataset incrementally. The model can be evaluated on the training dataset and on a hold out validation dataset after each update during training […]
Upcoming changes to the scikit-learn library for machine learning are reported through the use of FutureWarning messages when the code is run. Warning messages can be confusing to beginners as it looks like there is a problem with the code or that they have done something wrong. Warning messages are also not good for operational […]
Deep learning neural networks are relatively straightforward to define and train given the wide adoption of open source libraries. Nevertheless, neural networks remain challenging to configure and train. In his 2012 paper titled “Practical Recommendations for Gradient-Based Training of Deep Architectures” published as a preprint and a chapter of the popular 2012 book “Neural Networks: […]
