Subject archive for "machine-learning," page 5


Tensorflow, PyTorch or Keras for Deep Learning

Machine learning provides us with ways to create data-powered systems that learn and enhance themselves, without being specifically programmed for the task at hand. As machine learning algorithms go, there is one class that has captured the imagination of many of us: deep learning. Surely you have heard of many fantastic applications where deep learning is being employed. For example, take the auto industry, where self-driving cars are powered by convolutional neural networks, or look at how recurrent neural networks are used for language translation and understanding. It is also worth mentioning the many different applications of neural networks in medical image recognition.

By Dr J Rogel-Salazar13 min read


KNN with Examples in Python

In this article, we will introduce and implement k-nearest neighbours (KNN) as one of the supervised machine learning algorithms. KNN is utilised to solve classification and regression problems. We will provide sufficient background and demonstrate the utility of KNN in solving a classification problem in Python using a freely available dataset.

By Dr Behzad Javaheri14 min read

Machine Learning

Supervised vs. Unsupervised Learning: What’s the Difference?

Of all the thousands of algorithms available for machine learning, or ML, the vast majority use one of three main branches of learning techniques.

By David Weedmark8 min read

Machine Learning

Computer Vision in Deep Learning: An Introductory Guide

Computer vision is one of the most advanced and fascinating fields of data science, allowing computer technology to see as humans do and to represent data back to humans through visual imagery. While advances in computer vision have exploded just in the past couple of years, its roots and its first breakthroughs go back nearly 60 years.

By David Weedmark9 min read

Machine Learning

Powering Up Machine Learning with GPUs

Whether you are a machine learning enthusiast, or a ninja data scientist training models for all sorts of applications, you may have heard of the need to use graphical processing units (GPUs), to squeeze the best performance when training and scaling your models. This may be summarized by saying that training tasks based on small datasets that take a few minutes to complete on a CPU may take hours, days, or even weeks when moving to larger datasets if a GPU is not used. GPU acceleration is a topic we have previously addressed; see "Faster Deep Learning with GPUs and Theano".

By Dr J Rogel-Salazar14 min read

Machine Learning

A Guide to Natural Language Processing for Text and Speech

While humans have been using language since we arose, a complete understanding of language is a lifelong pursuit that often comes short, even for experts. To task computer technology with comprehending language, translating and even producing original written works represents a series of problems that are still in the process of being solved.

By David Weedmark7 min read

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