An example of clustering is a company that wants to segment its customers in order to better tailor products and offerings. Customers could be grouped on features such as demographics and purchase histories. Clustering with unsupervised learning is often combined with supervised learning in order to get more valuable results. In simplest terms, machine learning trains a machine to learn without being explicitly programmed how to do so. Coupon Marketing is a strategy used by businesses to lure customers to buy their products. Coupons are an easy and very commonly used strategy that can be used across several domains for… Read More

This was the inspiration for Co-Founders Jeet Raut and Peter Njenga when they created AI imaging medical platform Behold.ai. Raut’s mother was told that she no longer had breast cancer, a diagnosis that turned out to be false and that could have cost her life. Cross-validation allows us to tune hyper-parameters with only our training set. This allows us to keep the test set as a truly unseen data-set for selecting final model. Firstly, Deep Learning requires incredibly vast amounts of data (we will get to exceptions to that rule). The other is one-hot encoding, which means that each text… Read More

We know, for each applicant, specific values of different metrics that we think are important and relevant to solving their problem (e.g., their income, credit score, etc.). Another means of solving classification problems — and one that’s exceptionally well-suited to nonlinear problems — is the use of a decision tree. By adding more dimensions to the problem and allowing for nonlinear boundaries, we are creating a more flexible model. Although there are other prominent machine learning algorithms too—albeit with clunkier names, like gradient boosting machines—none are nearly so effective across nearly so many domains. With enough data, deep neural networks… Read More

Reinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovering errors or rewards. The most relevant characteristics of reinforcement learning are trial and error search and delayed reward. This method allows machines and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Machine Learning Set to Propel Fiber Placement to Next Level – Plastics Today Machine Learning Set to Propel Fiber Placement to Next Level. Posted: Mon, 30 Oct 2023 14:25:43 GMT [source] A second variation was created to train against a faster AI using… Read More

The world is growing at an exponential rate and so is the size of the data collected across the globe. The problem has shifted from collecting massive amounts of data to understanding it—turning it into knowledge, conclusions, and actions. Multiple research disciplines, from cognitive sciences to biology, finance, physics, and social sciences, as well as many companies believe that data-driven and “intelligent” solutions are necessary to solve many of their key problems. High-throughput genomic and proteomic experiments can be used to enable personalized medicine. Large data sets of search queries can be used to improve information retrieval. Historical climate data… Read More

Some types of anyons can be used to make what are called “topologically protected” qubits, which are stable against any small, local disturbances. The Wellcome Trust has selected the Cleveland Clinic-IBM Discovery Accelerator to develop proof-of-concept demonstrations of quantum computing for biologic and health applications through the Wellcome Leap Quantum for Bio Challenge. The complex mathematics behind these unsettled states of entangled ‘spinning coins’ can be plugged into special algorithms to make short work of problems that would take a classical computer a long time to work out… if they could ever calculate them at all. Quantum algorithms take a… Read More