AI News, Artificial Intelligence, Deep Learning, and Neural Networks, Explained artificial intelligence

Explained: Neural networks

In the past 10 years, the best-performing artificial-intelligence systems — such as the speech recognizers on smartphones or Google’s latest automatic translator — have resulted from a technique called “deep learning.” Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years.

Neural nets were a major area of research in both neuroscience and computer science until 1969, when, according to computer science lore, they were killed off by the MIT mathematicians Marvin Minsky and Seymour Papert, who a year later would become co-directors of the new MIT Artificial Intelligence Laboratory.

An object recognition system, for instance, might be fed thousands of labeled images of cars, houses, coffee cups, and so on, and it would find visual patterns in the images that consistently correlate with particular labels.

To each of its incoming connections, a node will assign a number known as a “weight.” When the network is active, the node receives a different data item — a different number — over each of its connections and multiplies it by the associated weight.

If the number exceeds the threshold value, the node “fires,” which in today’s neural nets generally means sending the number — the sum of the weighted inputs — along all its outgoing connections.

Training data is fed to the bottom layer — the input layer — and it passes through the succeeding layers, getting multiplied and added together in complex ways, until it finally arrives, radically transformed, at the output layer.

Perceptrons were an active area of research in both psychology and the fledgling discipline of computer science until 1959, when Minsky and Papert published a book titled “Perceptrons,” which demonstrated that executing certain fairly common computations on Perceptrons would be impractically time consuming.

If you think of this as this competition between analog computing and digital computing, they fought for what at the time was the right thing.” Periodicity By the 1980s, however, researchers had developed algorithms for modifying neural nets’ weights and thresholds that were efficient enough for networks with more than one layer, removing many of the limitations identified by Minsky and Papert.

The complex imagery and rapid pace of today’s video games require hardware that can keep up, and the result has been the graphics processing unit (GPU), which packs thousands of relatively simple processing cores on a single chip.

Parts two and three, which have been released as CBMM technical reports, address the problems of global optimization, or guaranteeing that a network has found the settings that best accord with its training data, and overfitting, or cases in which the network becomes so attuned to the specifics of its training data that it fails to generalize to other instances of the same categories.

AI, Machine Learning, Deep Learning Explained in 5 Minutes

if a bot follows the following preprogrammed algorithm, it will never lose a game: (courtesy of Wikipedia) Now, an algorithm like this doesn’t possess the cognitive, learning, or problem solving abilities that most people associate an “AI” with.

Arthur Samuel coined the phrase “Machine Learning”in 1959, defining it as “the ability to learn without being explicitly programmed.” Machine Learning, at its most basic form, is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.

A house price prediction model looks at a ton of data, with each data point having several dimensions like size, bedroom count, bathroom count, yard space, etc.

Concisely, Unsupervised Learning just finds similarities in data — in our house example, the data wouldn’t include house prices (the data would only be input, it would have no output) and the model would be able to say “Hmm, well based on these parameters, House 1 is most similar to House 3” or something of the sort, but wouldn’t be able to predict the price of a given house.

Reinforcement Learning is best explained with a simple, brief, diagram: An agent takes actions in an environment, which is interpreted into a reward and a representation of the state, which are fed back into the agent.

Artificial Neural Networks Explained

The Leaky ReLU activation function works the same way as the ReLU activation function except that instead of replacing the negative values of the inputs with 0 the latter get multiplied by a small alpha value in an attempt to avoid the “dying ReLU” problem.

another way to think of it: without a non-linear activation function in the network, an artificial neural network, no matter how many layers it has, will behave just like a single-layer perceptron, because summing these layers would give you just another linear function.

if you use the sigmoid activation function then you would be hit by the vanishing gradient problem (the vanishing gradient problem is when the gradient becomes so small in the earlier layers of a deep neural network to the point that it barely affects the weights of the earlier layers thus failing at optimizing our initial weights.), should we use the tan-h activation function then?

Once a ReLU ends up in this state, it is unlikely to recover, because the function gradient at 0 is also 0, so gradient descent learning will not alter the weights.), here comes the Leaky/Parametric ReLU to rescue and instead of outputting a flat out zero for the negative values the Leaky ReLU multiplies the negative values by an alpha parametric value (α=0.01).

Forward propagation is the process of feeding the Neural Network with a set of inputs to get their dot product with their weights then feeding the latter to an activation function and comparing its numerical value to the actual output called “the ground truth”.

Let’s demonstrate how the output gets calculated in a forward pass in a 3–4–1 Artificial Neural Network: The initial weights of the first and second layer: Input: output: The activation function of the hidden layer will be the ReLU function and the activation function of the output layer will be the Sigmoid function.

This is in a nutshell how a forward propagation works and how a neural network generates its predictions, the forward propagation step has always been less mathematically intense than the backpropagation step where most students and beginners find legitimately harder to grasp.

first let’s lay some important derivatives: Let’s now demonstrate the calculations that go underneath the Backpropagation process: An illustration of how a neural network backpropagates its error: let’s now predict the output with the new updated weights.

the ANN’s architecture will look like this: Your results should be like this: Vizualization of the loss function: As you can see in the results the cross entropy loss has dramatically decreased from the first 1000th iteration to the last 1000th iteration which showcase how good our neural network is doing to find the optimal weights.

Neural Networks Explained - Machine Learning Tutorial for Beginners

This video provides beginners with an easy tutorial explaining how a neural network works - what math is involved, and a step by step explanation of how the data moves through the network.The example used will be a feed forward neural network with back propagation.

But what *is* a Neural Network? | Deep learning, chapter 1

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