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Given a set of images with a relatively high degree of separation between classes, it is perfectly feasible to train a CNN to classify those images on a typical laptop or PC. A great example is the famous MNIST dataset, which contains 60,000 training images of scanned, handwritten digits, each measuring 28 x 28 pixels,…

Computer vision is a branch of AI in which computers extract perceptual information from images. Real-world uses include identifying objects in photos, removing inappropriate images from social-media sites, flagging defective parts coming off an assembly line, and facial recognition. Computer-vision models can even be combined with natural language processing (NLP) models to caption photos. I…

It’s not difficult to use Scikit-learn to build machine-learning models that analyze text for sentiment, identify spam e-mails, and classify textual data in other ways. But state-of-the-art text classification is most often performed with neural networks. You already know how to build neural networks that do binary classification and multiclass classification using rows of numbers…

My previous post described how to build a neural network that serves as a binary classifier. Here’s a binary classifier that accepts two inputs, has a hidden layer with 128 neurons, and outputs a value from 0.0 to 1.0 representing the probability that the input belongs to the positive class: Key elements include an output…

One of the common uses for machine learning is performing binary classification, which looks at an input and predicts which of two possible classes it belongs to. Practical uses include sentiment analysis, spam detection, and credit-card fraud detection. Such models are trained with datasets labeled with 1s and 0s representing the two classes, employ popular…

Machine learning isn’t hard when you have a properly engineered dataset to work with. The reason it’s not hard is libraries such as Scikit-learn and ML.NET, which reduce complex mathematical manipulations to a few lines of code. Deep learning isn’t hard, either, thanks to libraries such as the Microsoft Cognitive Toolkit (CNTK), Theano, and PyTorch.…

Deep learning is a subset of machine learning that relies primarily on neural networks. Most of what’s considered AI today is accomplished with deep learning. From recognizing objects in photos to real-time speech translation to using computers to generate art, music, poetry, and photorealistic faces, deep learning allows computers to perform feats of magic that…