machine learning
Convergence refers to the point in machine learning training where ideal weights have been discovered and cost function minimized. But this is, at times, plagued by issues of local minima and slow learning. Here Mini-batch gradient descent comes handy, which combines the advantages of batch gradient learning as well as stochastic gradient descent. The process […]
Artificial Intelligence and Machine Learning are particularly useful for complex applications like Image Recognition, such as distinguishing a puppy face from a muffin ! Advanced Machine Learning algorithms, which fall under Deep Learning category, include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative-Adversarial Networks (GANs) etc. The CNNs are extremely potent solutions for image […]
Recurrent Neural Networks or RNNs are a type of deep neural networks which help in sequence modeling. They are very useful for processing data that is sequential in nature, such as spoken language, stock prices or weather patterns. One can use RNNs to build applications in natural language processing (such as sentiment analysis), language translation […]
Unsupervised Learning means that we do not have labelled data. In other words, input data samples do not have labeled output. We have input features x1, x2, x3… etc. for the available data samples, but not output y. With this kind of data, supervised learning algorithms like regression, decision trees etc. are not an option. […]