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Machine Learning for Zombies

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Multilayer Perceptrons (MLP), are complex algorithms that take a lot of compute power and a *ton* of data in order to produce satisfactory results in reasonable timeframes. Let's start with what they're not: neural networks, despite the name and every blog post and intro to machine learning text book you've probably read up till now, are not analogs of the human brain. There are some *very* surface-level similarities, but the actual functionality of a neural network has almost nothing in common with the way the neurons that make up the approximately three pounds of meat that sits between your ears and defines everything you do and how you experience reality. Just like a lot of other machine learning algorithms, they use the formula "label equals weight times data value plus offset" (or y w*x b) to define where they draw their lines/hyperplanes for making predictions. In machine learning, that slope is called a weight.)


What is Deep Learning and How Does it Work?

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At a very basic level, deep learning is a machine learning technique. It teaches a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of images, text, or sound. The inspiration for deep learning is the way that the human brain filters information. Its purpose is to mimic how the human brain works to create some real magic. In the human brain, there are about 100 billion neurons. Each neuron connects to about 100,000 of its neighbors.


What is Deep Learning and How Does it Work?

#artificialintelligence

At a very basic level, deep learning is a machine learning technique. It teaches a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of images, text, or sound. The inspiration for deep learning is the way that the human brain filters information. Its purpose is to mimic how the human brain works to create some real magic. In the human brain, there are about 100 billion neurons. Each neuron connects to about 100,000 of its neighbors.


What is Deep Learning and How Does it Work?

#artificialintelligence

Sit back, relax, and get comfortable with cool concepts like artificial neural networks, gradient descent, backpropagation, and more. The inspiration for deep learning is the way that the human brain filters information. At a very basic level, Deep Learning is a Machine Learning technique. It teaches a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of images, text, or sound. The inspiration for Deep Learning is the way that the human brain filters information. Its purpose is to mimic how the human brain works to create some real magic.


Understanding Deep Neural Networks from First Principles: Logistic Regression

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The advanced feats we've seen machines do thus far have basically been examples of clever optimization techniques). So what does this learning process look like? First, weight and bias values are propagated forward through the model to arrive at a predicted output. At each neuron/node, the linear combination of the inputs is then multiplied by an activation function as described above-- the sigmoid function in our example. This process by which weights and biases are propagated from inputs to output is called forward propagation. After arriving at the predicted output, the loss for the training example is calculated.