Everything you need to know about Convolution Neural Nets

Convolutional neural nets

Machine Learning has been around for a while now and we are all aware of its impact in solving everyday problems. Initially, it was about solving simple problems of statistics, but with the advancements in technology over time, it picked up pace to give bigger and better results. It has grown to solve bigger problems such as image recognition and now even possesses the ability to distinguish a cat from a dog.

In this article, we will briefly touch upon the nature and how to manipulate information represented through the network to solve some of the toughest problems around image recognition.

Prologue: a troublesome story of Real Estate Agents

Let’s start right at the beginning. Say we have input vectors — specifications of a house, and outputs like the price of the house. Not delving deeper into the details, visualize it as though we have information described as a set of concepts such as kitchen size, number of floors, location of the house and we need to represent information pertinent to another set of concepts such as the price of house, architecture quality, etc. This is basically conversion from one conceptual representation to another conceptual representation. Let’s now look at a human converting this –

He (say Alex) would probably have a mathematical way to convert this from one conceptual representation to another through some ‘if-else’ condition to start off.

If he (say Bob) was slightly smarter, he would have converted input concepts into some intermediary scores like simplicity, floor quality, noise in the neighbourhood, etc. He would also cleverly map these scores to the corresponding final output, say price of the house.

If you see what has changed from an ordinary real estate agent(Alex) to a slightly smarter real estate agent (Bob) is that he mapped input-output information flow in detail. In other words, he changed the framework in which he thought he could best represent the underlying architecture.

Lesson 1: The ‘Framework of thinking’ is everything

So the difference between Alex and Bob’s thought process was that Bob could figure out that secondary concepts are easy to calculate, and hence he combined them to represent the final desired output whereas Alex tried to apply an entire ‘if-else’ logic for each one of the input variables and mapped it with each one of the output variables. Bob in a way represented the same mapping in a more systematic way by breaking them into smaller concepts and just had to remember fewer concepts. Meanwhile, Alex had to remember how every input is connected to every output without breaking it into smaller concepts. So the big lesson here is that the ‘framework of thinking’ is everything.

This is what most researchers have realized. Every researcher has the same problem, let’s take for instance, the cat vs dog image.

Researchers have to convert information from one conceptual representation (pixels) to another conceptual representation (is-cat is True/False). They also have almost the same computational power(memory, complexity etc), hence the only way to solve this problem is to introduce the framework of thinking that decodes inputs with minimum resources and converts it from one form to another. You would’ve already heard about a lot of ‘frameworks of thinking’. When people say Convolutional Networks, it simply means — it is a framework of representing a particular mapping function. Most statistical models that predict house prices are also just mapping functions. They all try to best predict a universal mapping function from input to output

Lesson 2: Universal Mapping function like Convolutional Neural Networks

Convolutional Neural Networks or CNN are a form of functions that uses some concepts around images — like positional invariance. That means the network can re-use the same sub mapping function from the bottom part of the image to the top part of the image. This essentially reduces the number of parameters in which the Universal Mapping function can be represented. This is why CNNs are cone shaped. Here we move from concepts that are space oriented (pixels) to concepts that are space independent (cat-or-not, has-face). That’s it. It’s that simple. Information is smartly converted from one form to another.

Lesson 3: Convolutional Neural Networks and the Brain

Recent advancements in Neuroscience has essentially said the same thing regarding how we decode the information in the visual cortex. We first decode lines, then decode objects like boxes, circles, curves etc, then decode them into faces, headphones etc.


A lot of Machine Learning/Deep Learning/AI technologies have very simple conceptual frameworks. The reason behind it solving gargantuan problems lies in the complexity that arises from a whole lot of simple-conceptual-frameworks that are attached end-to-end. It is so complex that we can’t really predict whether these networks can solve any kind of problem. Yet, we have been implementing them on a day to day basis based on some sort of assumption. It’s very similar to the human brain. We know its underlying structure and framework. We discovered it half a century ago. Yet, we’ve not been able to decipher this complex world and we are still unsure as to when we’ll reach such an understanding.

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