Every neural network needs at least one activation function to make accurate predictions. It’s at the heart of the processing capabilities. Choosing the right one can result in a precise, high-performance network consistently delivering the desired results. Data scientists can use an easy process to determine... Read more

Neural networks are extremely convenient. They are usable for both regression and classification, work on structured and unstructured data, handle temporal data very well, and can usually reach high performances if they are given a sufficient amount of data. What is gained in convenience is, however,... Read more

Deep learning is bringing many benefits to the world: solving the 50-year-old protein folding problem, detecting cancer, and improving the power grid. While there is so much that deep learning is powering, we also need to consider the costs. In the quest for more accurate and... Read more

Distillation is a hot research area. For distillation, you first train a deep learning model, the teacher network, to solve your task. Then, you train a student network, which can be any model. While the teacher is trained on real data, the student is trained on the teacher’s outputs. It... Read more