artificial neural network (annit is an efficient computing systemwhose central theme is borrowed from the analogy of biological neural networks neural networks are one type of model for machine learning in the mid- and early smuch important architectural advancements were made in neural networks in this you will learn more about deep learningan approach of ai deep learning emerged from decade' explosive computational growth as serious contender in the field thusdeep learning is particular kind of machine learning whose algorithms are inspired by the structure and function of human brain machine learning / deep learning deep learning is the most powerful machine learning technique these days it is so powerful because they learn the best way to represent the problem while learning how to solve the problem comparison of deep learning and machine learning is given belowdata dependency the first point of difference is based upon the performance of dl and ml when the scale of data increases when the data is largedeep learning algorithms perform very well machine dependency deep learning algorithms need high-end machines to work perfectly on the other handmachine learning algorithms can work on low-end machines too feature extraction deep learning algorithms can extract high level features and try to learn from the same too on the other handan expert is required to identify most of the features extracted by machine learning time of execution execution time depends upon the numerous parameters used in an algorithm deep learning has more parameters than machine learning algorithms hencethe execution time of dl algorithmsspecially the training timeis much more than ml algorithms but the testing time of dl algorithms is less than ml algorithms approach to problem solving deep learning solves the problem end-to-end while machine learning uses the traditional way of solving the problem by breaking down it into parts convolutional neural network (cnnconvolutional neural networks are the same as ordinary neural networks because they are also made up of neurons that have learnable weights and biases ordinary neural networks