DNNs are artificial neural networks with multiple hidden layers between input and output. Hidden layers enable complex pattern learning and data processing. Networks typically have 100+ hidden layers between input and output nodes
Deep learning enables machines to automatically learn high-level feature representations. Tutorial is suitable for beginners to intermediate readers. Deep learning is essential for data science and machine learning
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Neural networks are inspired by biological brain structures. Artificial neurons connect through edges to process signals. Networks consist of input, output, and hidden layers. Weights determine signal strength between neurons
Deep learning uses neural networks for classification, regression and representation learning. Deep refers to the number of layers in neural networks (3-several hundred). Deep learning can be supervised, semi-supervised or unsupervised
Perceptron takes inputs and produces binary output using weights. Output is compared with training set outputs for learning. Perceptron implementation uses numpy matrix operations