Convolutional Neural Networks are specialized types of neural networks which employ convolution, an advanced type of mathematical operation used to recognize patterns within images such as edges, curves or entire shapes such as eyes. Convolution layers add a sliding function on top of each row of pixels to produce feature maps, while pooling layers downsample…
Supervised learning involves data points which have been assigned a desired output value, either categorically (like red or blue) or continuously (such as house price or customer churn rate), such as redness or blueness. It can be applied both for classification and regression problems. With clear objectives and labeled training data, supervised models tend to…
Reinforcement learning is a type of machine learning algorithm that trains computers through trial-and-error to take actions with predictable consequences over time, like robotic navigation or stock trading. It works especially well in situations in which decisions take longer to have their full effects realised, like robotic navigation or trading stocks. These algorithms, which utilise…
Neural networks, loosely inspired by human brain function, recognize patterns by labeling or clustering raw data. Over time, neural networks learn by gradually adjusting their adjustable weights until they can map input features correctly to output labels. This method is known as supervised learning. Any label that correlates to data can be used to train…
Machine learning is an algorithm capable of scanning large volumes of data to identify trends and patterns that would be hard for humans to spot, making machine learning useful in numerous fields such as e-commerce, financial forecasting, fraud detection and autonomous driving cars. Software like this helps reduce costs by sorting past and present data…
Machine learning (ML) algorithms enable computers to make decisions or predictions without being explicitly programmed for this task. Machine learning processes massive volumes of data faster than human beings can, uncovering patterns which would otherwise be difficult or impossible to spot with conventional analysis techniques. Machine learning (ML) has many applications. Here are some key…
