Math for Deep Learning: What You Need to Know to Understand Neural Networks by Ronald T. Kneusel Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use deep learning toolkits. With Math for Deep Learning, you will learn the essential math and background used in deep learning. You will learn key deep learning-related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus through Python examples, as well as how to implement data flow in neural networks, backpropagation, and gradient descent. You will also use Python to study the math behind these algorithms and even build a fully functional neural network. In addition, you will learn about gradient descent, including variants commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.
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