Abstract
The chapter will introduce the readers to the latest state-of-the-art deep learning algorithms from scratch. Deep learning is a modern field of machine learning capable of understanding the underlining patterns in the data on its own and identifying the nature of the data. This chapter will travel through all the algorithms, from basic neural network structure to advanced neural networks, such as convolution neural networks and recurrent neural networks. It covers artificial neural networks, perceptron learning algorithms, convolution neural networks, recurrent neural networks, long short term memory, and essential concepts such as backpropagation, gradient descent, activation functions, and optimizations. With the hands-on example and Pythonic approach to real-world applications, this chapter will enhance the readers' knowledge of advanced technologies.
Keywords: Deep Learning, Neural Network, Perceptron Learning Algorithm, MLP, ANN, Convolutional Neural Network, Random Forest, K-nearest Neighbor, Naïve Bayes Classification, Support Vector Machine.
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