AI News, Artificial neural network artificial intelligence

The Most Intuitive and Easiest Guide for Artificial Neural Network

And you probably have seen the awesome works such as image classification, computer vision, and speech recognition.

There are already hundreds of articles explaining the concept of the artificial neural network with the name of “a beginner’s guide on back propagation in ANN” or “A gentle introduction of the artificial neural network.” They are really great already, but I found It could be still hard for someone who is not comfortable with mathematical expressions.

The complete set of this post is like follows: Today, I’m going to explain the basics of artificial neural network (ANN) with the least amount of maths.

This could be the easiest and the most intuitive explanation ever, so if you’re a math hater or having trouble with linear algebra, come and take your piece!

Today’s keywords are forward propagation, activation functions, backpropagation, gradient descent and weight updating.

They wanted to apply the biological processes in the brain to mathematical algorithms and from that point, neural research field was split into two ways.

When we approach the destination, we’ll sum up all the possible scores and determine which island is the best one we were looking for.

One Interesting part here is that when the boat teams approach the output layer, they come back to the input layer.

This word could sound unfamiliar to you, but we already have been using them with other machine learning algorithms such as lasso regression or boosting algos, controlling the coefficient of features in other regression.

If we just use linear calculation without activation functions, just like what we’ve done above, we can’t give any ‘hidden layer effect’ to our model.

To ‘activate’ the real power of neural networks, we need to apply an ‘activation function.’ Cause activation functions help the model to capture non-linearities within the data.

AI Created in DNA-Based Artificial Neural Networks

AI-based pattern recognition has a wide variety of real-world uses, such as medical diagnostics, navigation systems, voice-based authentication, image classification, handwriting recognition, speech programs, and text-based processing.

Pioneering researchers led by Dr. Lulu Qian at the California Institute of Technology (Caltech) have created synthetic biochemical circuits that are able to perform information processing at the molecular level–an artificial neural network consisting of DNA instead of computer hardware and software.

Artificial intelligence is in the early stages of a renaissance period—a rebirth that is largely due to advances in deep learning techniques with artificial neural networks that have contributed to improvements in pattern recognition.

Specifically, the resurgence is largely due to a mathematical tool that calculates derivatives called backpropagation (backward propagation)—it enables artificial neural networks to adjust hidden layers of neurons when there are outlier outcomes for more precise results.

The recent trends of falling costs of DNA sequencing, increasing amounts of big data, lower barriers in gene editing via CRISPR, decreasing computing storage and processing costs, decentralized cloud-based computing, and breakthrough advances in AI deep learning algorithms, have contributed to advancing both genomics and synthetic biology.

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