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To grasp the idea of deep learning, imagine a family, with an infant and parents. The toddler points objects with his little finger and always says the word ‘cat.’ As his parents are concerned about his education, they keep telling him ‘Yes, that is a cat’ or ‘No, that is not a cat.’ The infant persists in pointing objects but becomes more accurate with ‘cats.’ The little kid, deep down, does not know why he can say it is a cat or not. He has just learned how to hierarchies complex features coming up with a cat by looking at the pet overall and continue to focus on details such as the tails or the nose before to make up his mind. A neural network works quite the same. Each layer represents a deeper level of knowledge, i.e., the hierarchy of knowledge. A neural network with four layers will learn more complex feature than with two layers.

Deep learning Process A deep neural network provides state-of-the-art accuracy in many tasks, from object detection to speech recognition. They can learn automatically, without predefined knowledge explicitly coded by the programmers.

What is Deep Learning? Each Hidden layer is composed of neurons. The neurons are connected to each other. The neuron will process and then propagate the input signal it receives the layer above it. The strength of the signal given the neuron in the next layer depends on the weight, bias and activation function. The network consumes large amounts of input data and operates them through multiple layers; the network can learn increasingly complex features of the data at each layer.

What is Deep Learning? Deep Learning is a computer software that mimics the network of neurons in a brain. It is a subset of machine learning based on artificial neural networks with representation learning. It is called deep learning because it makes use of deep neural networks. This learning can be supervised, semi-supervised or unsupervised. Deep learning algorithms are constructed with connected layers. The first layer is called the Input Layer The last layer is called the Output Layer All layers in between are called Hidden Layers. The word deep means the network join neurons in more than two layers.

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We'll begin with the first part of deep learning tomorrow

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