Nov 14, 2022 | By
We often hear the terms Artificial Intelligence (AI), Machine Learning (ML), and Deep learning (DL) getting used interchangeably. However, there is a subtle difference between all of these let’s see what they actually mean.
As mentioned above, it is inspired by the neural architecture in the human brain which processes and gets insights by passing data through different layers. DL demands huge data as it has to train multiple parameters in each layer.
Deep Learning was dated back to the 1940s with the invention of a neural network-based model called the “Perceptron”.
Then why the buzz around it started only in the past few years? Let’s look at a few reasons behind the buzz.
We are living in a digital world where everything is getting digitized.
You might have observed this shift, where every business is integrating software solutions to ease the process. Even government policies and schemes are getting launched and implemented online.
We are witnessing digital payments boom and what about content that is adding up on social media platforms like Facebook, Insta, Twitter, Whatsapp, etc?
With approx. 63% of the world's total population having access to the internet and the volume of data that is getting generated every day is mind-boggling.
Back in the 2000’s normal laptops used to come with a RAM capacity of 500MB or so and it used to take minutes to run even small programs.
How about now? Everyone is using 16GB RAM and more, which can run complex programs in seconds.
Also, we are using specialized hardware like GPUs and TPUs. These come up with large processing power that helps in running parallel jobs and reduces execution time. Nowadays laptops are coming up with built-in GPUs (Graphic cards).
Google TPUs (Tensor processing units) are designed specifically to handle neural network loads.
All these advancements in hardware are helping deep learning models to run complex jobs within less time.
Back then we used to do programming using complicated languages like C++, Java, and similar ones which demands high knowledge of software systems.
Coming to Python, is one of the most accessible and interpretable languages ever with simple syntax. Anyone can learn it in no time and start building applications, unlike other languages.
Even people without computer science backgrounds like mathematicians, statisticians, etc found it simple to learn python and implement deep learning algorithms with less effort in learning to code.
These days we are also witnessing the trend of people moving from non-CS backgrounds to the data science domain, thanks to Python for making the transition easier.
And the other big thing is the open source ecosystem. Mostly used deep learning frameworks like Tensorflow (by Google), PyTorch (by Facebook), etc are all open-sourced. Anyone can install the libraries and start using them for free. Isn't it cool?
Have you heard of something like Azure ML, AWS Sagemaker, and all? These are ML studios in the cloud, even anyone without prior experience in running ML/DL algorithms can use these platforms to build models and generate some predictions.
Entry barriers to start experimentation with deep learning techniques are getting removed with the advent of these kinds of cloud platforms.
How many of you are using Google Colab notebooks with GPU instances? As running deep learning models becomes speedy and simple with advanced hardware, even if you don't have that configured in your machine you can simply run them on cloud platforms.
Also with these platforms like Google colab in place, no need to worry about software installations to make your model up and running.
We are in an AI Boom, Data is the new fuel and all the companies want to harness some insights from the huge volume of data that is getting created to better understand their customer's pulse and to stand ahead in the competition.
Google has announced that it is moving from Mobile first approach to the AI-first approach which itself speaks about AI growth. This kind of move from one of the tech giants further accelerates the growth of deep learning techniques.
These are some of the crucial reasons behind the exponential growth in the use of Deep Learning techniques. Let’s see where the future of Deep learning takes us.