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Video Summaries of Deep Learning

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1-Page Summary of Deep Learning

“Deep learning” is based on artificial neural networks, which simulate how the brain learns through experience.

Information is critical to learning. Knowledge comes from information and understanding of what people do, want, and are like. Now machines can learn by themselves about the world through deep learning systems. Deep learning machines analyze vast amounts of data to make sense of the world in a way that humans cannot. It’s based on mathematics, computer science, and neuroscience. Machines need lots of data to be able to learn how things work so they can become better at doing them or making decisions for us in the future.

Deep learning is not only about the evolution of artificial intelligence, but also human intelligence. The research community has been working on this for three decades and it’s finally coming to fruition now.

The first artificial neural network involved a “perceptron,” which weighs inputs and outputs, similar to how a brain neuron functions.

Humans are more than just logical beings. They use general intelligence to solve problems that require specialized knowledge. Learning helps build this intelligence, which is why it’s important for artificial intelligences (AIs) to learn like humans do: by executing algorithms within “massively parallel architectures.” The first neural network was created in 1962 at Cornell University by Frank Rosenblatt and modeled on the brain’s processes with one layer of inputs and one output unit. This network assigned a value based on weights – or connections – between its input units and its output unit; if an image matched what the network had learned about cats, then it would assign a 1 to the cat category.

Programmers can design the weights of a neural network by hand, but it’s better to automate the process so that computers learn from examples. The goal is for them to produce generalizations based on many specific examples. If there aren’t enough examples, then they’ll simply memorize what they’ve learned and won’t be able to generalize beyond those specific cases.

The next level of complexity is an independent component analysis. This has more than one output layer and uses the measure of independence between output units as the cost function. The independent outputs perfectly separate the data, or “decorrelate” it. Feedback connections to earlier hidden layers and recurrent connections among units at each layer add complex patterns. Independent components start by being densely coded but then become sparsely coded as information distributes to higher levels over time in this network design

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The process of explaining how the brain works is similar to a pyramid. The base of the pyramid starts with dense molecular processes and moves up through synapses, neurons, networks, maps and systems in order to explain the entire central nervous system. Synapses are the computational elements of the brain that aren’t fully understood. A new field called “computational neuroscience” tries to investigate this further by studying it at all levels from molecules to cells and circuits along with their interactions as well as computations performed on those interactions.

The Hopfield net and the Boltzmann machine expanded artificial neural networks and made them more efficient.

There are two types of neural network models, scruffy and neat. Scruffy networks represent objects by distributing the representation across many units. Neat networks have one label per unit and give more accurate results. To make progress in this area, it’s important to combine these two approaches because each has its advantages. The key is to build feedback connections between layers instead of simply feedforward ones.