# AI News, Convolutional Neural Networks: The Biologically-Inspired Model

- On Sunday, June 3, 2018
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## Convolutional Neural Networks: The Biologically-Inspired Model

Source: https://medium.com/@alonbonder/ces-2018-computer-vision-takes-center-stage-9abca8a2546d The field of Computer Vision tackles this exact problem, as machine learning researchers have focused extensively on object detection problems over time.

In particular, Computer Vision researchers use neural networks to solve complex object recognition problems by chaining together a lot of simple neurons.

In a traditional feed-forward neural network, the images are fed into the net and the neurons process the images and classify them into the outputs of True and False likelihood.

Feed-forward neural nets only works well when the digit is right in the middle of the image, but fails spectacularly when the digit is slightly off position.

This allows the network to have lots of neurons and express computationally large models while keeping the number of actual parameters — the values describing how neurons behave — that need to be learned fairly small.” Source: A 2D CNN - http://colah.github.io/posts/2014-07-Conv-Nets-Modular/ Note the term being used there: identical copies of the same neuron.

In the early 1990s, LeCun worked at Bell Labs, one of the most prestigious research labs in the world at the time, and built a check-recognition system to read handwritten digits.

The first half describes convolutional nets, shows its implementation, and mentions everything else related to the technique (which I’ll cover in the CNN Architecture section below).

The big takeaway is that you can build a CNN system and train it to simultaneously do recognition and segmentation, and provide the right input for the language model.

However, the sigmoid non-linearity has a couple of major drawbacks: (i) sigmoids saturate and kill gradients, (ii) sigmoids have slow convergence, and (iii) sigmoid outputs are not zero-centered.

CNN uses max-pooling, in which it defines a spatial neighborhood and takes the largest element from the rectified feature map within that window.

From a bigger picture, a CNN architecture accomplishes two major tasks: feature extraction (convolution + pooling layers) and classification (fully-connected layers).

There are a hundred times as many classes, a hundred times as many pixels, two dimensional images of three-dimensional scene, cluttered scenes requiring segmentation, and multiple objects in each image.

Looking at AlexNet's architecture below, you can identify the main differences between it and LeNet: AlexNet became the pioneering “deep” CNN that won the competition with 84.6% accuracy, while the second-place model (which still used the traditional techniques in LeNet instead of deep architectures), only achieved 73.8% accuracy rate.

CNN architectures continue to feature prominently in Computer Vision, with architectural advancements providing improvements in speed, accuracy, and training for many of the applications and tasks mentioned below: Source: https://idealog.co.nz/tech/2014/11/googles-latest-auto-captioning-experiment-and-its-deep-fascination-artificial-intelligence CNNs have also found many novel applications outside of Vision, notably Natural Language Processing and Speech Recognition: Source: http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/ Let’s revisit our example of the Harry Potter image again and see how I can use CNN to recognize its features: As you can see from this article, Convolutional Neural Networks played an important part in shaping the history of deep learning.

Heavily inspired by the study of the brains, CNNs performed extremely well in commercial applications of deep learning (vision, language, speech) compared to most other neural networks.

Research into CNN architectures advances at such a rapid pace: using fewer weights/parameters, automatically learning and generalizing features from the input objects, being invariant to object position and distortion in image/text/speech, etc.

- On Tuesday, March 26, 2019

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