Introduction to Deep Learning: A Beginner's Guide

This means that the amount of information that worldwide organizations collect has increased tremendously. This data comes in both structured and unstructured forms, and sorting and analyzing it to make it useful gave headaches to everyone tasked with doing so. So here, Deep Learning comes into play!

Deep Learning is a subset of a broader category of Artificial Intelligence, and to understand it, we first need to grasp what Machine Learning and neural networks are. 

Machine Learning might sound intimidating at first, but this is actually what it sounds like. Machines are learning. And as a data science that has seen huge levels of attention lately, ML is everywhere around us. It collects information and uses statistical methods to mine data, makes predictions, and offers useful insights. From Netflix’s recommendation algorithm to spam filtering and process automation, the use cases for ML can go on and on. Scientists and engineers are working to make ML better every day by improving algorithms to learn from historical data and become more accurate in responses by trial and error, just like the human brain does.

On the other hand, a neural network is a subclass of Machine Learning. This technology mimics the human brain by creating nodes that play the part of a neuron. This helps computers process data in a more targeted way, making connections and finding relationships between subsets of data. Resembling the system of neurons in our brain, neural networks can adapt to imputed data on the go, offering us the best result without changing the output criteria. It has been used for quite some time in a wide range of applications. In finance, for example, neural networks work to predict the future movements of ever-changing stock performance. 

With deep AI, computers are trained to further reproduce the way humans think and learn, making machines take on really impressive tasks that would require human intelligence otherwise. It can process and recognize patterns in all kinds of data, from text to pictures and even sounds. And because of this, DL powers the development of AI as a whole that we have seen disrupting every industry in the last years. 

By using deep tech models, applications like ChatGPT can understand and respond to text in a second, describe images, and offer beneficial information to all of us. But what we need to understand is that deep learning has a huge pool of use cases, as it can automate a lot of tasks and spot patterns in voice input, images, and real-time data.

DL is making machines capable of a very impressive capability - feature extraction from images that result in a very accurate recognition. And it’s all possible through big data! What scientists managed to do with deep tech blows our minds. By dividing an image into multiple layers of patterns and information and then feeding it to a neural network, they can train a deep learning model to process pictures like never before. 

The old way of image processing was done by manually inputting rules that would help a machine extract features from an image. But with deep AI, we only need to feed the algorithm images and label them as what they are, and the neural network will learn from everything in that picture - colors, patterns, edges, and many other features. 

So, we are not talking about having a machine sort pictures of cats and dogs from a set. DL has long passed that threshold with big data. Nowadays, it is able to recognize objects in images and even identify artifacts such as noise or any improper feature that might make the original object in an image harder to recognize. 

Think about how this technology can be leveraged on social media, for example. By deploying a DL model to learn from user pictures, a brand can gain valuable insights into its products' use and by whom. Also, social media apps can spot and block potential misinformation or unwanted content posted by users, like violent and nudity posts. 

By training Deep Neural Networks (DNNs) to different kinds of recorded voice inputs, scientists created the chance for digital assistants to be created. Nowadays, Alexa, Siri, or Google Assistant can listen to our questions and deliver accurate answers that have proven to make our lives easier. Even more so, by using Deep Learning for speech recognition, machines are able to convert vocals into text, offering us the ability to create text by using just our voice. 

Virtual assistants are trained through machine learning and big data to respond to sound waves that they learned mean a certain command. Think about how Alexa or any other voice assistant “wakes up” to a specific call to action - like hearing their name. From there, all the sounds that we produce are registered and transformed into specific letter combinations, and predictions of the best output are offered to us. People use such assistants to ask questions and access the internet without moving a finger. Alexa can even connect us to all the other smart devices in our house. Think about how some people turn on their lights by just asking their digital home assistant to do so. 

Think about it like this - for a vehicle to be able to navigate the roads, it needs to process a lot, and we mean a LOT, of information in real-time. But it cannot just process everything for the first time and still be efficient at high-scale implementation. Roads are already busy with other cars, pedestrians, and sometimes animals, so new combinations of scenarios occur every day. 

But here, Deep Learning can make a huge difference by giving a car the power to maneuver every possible scenario. The technology is not fully there yet, and we might wait a few years before fully self-driven cars are going to become the norm. But for now, we need to understand that by deploying DL technology in a car’s computer, the vehicle will have a neural network able to access both information that it had previously been fed and also analyze and predict the best action to take in a real-time scenario. 

And because of this whole learning by-data process, time and miles covered are playing a huge role in the improvement of the self-driving experience. The more data companies are able to collect for their DNN models inside the cars, the better prepared their software will be. 

Cars can collect information both from already existing scenarios or by collecting it in real-time via radar, lidar, camera, and different sensors. For example, Tesla is primarily using video information recorded by 8 cameras assembled on the car. These cameras offer a 360-degree video panorama, and for every mile that a Tesla spends on the road, the system learns the best actions to take in a multitude of scenarios. And because of that, Tesla is currently one of the biggest players in the self-driving car movement. With billions of miles of real-road scenarios covered, they have a huge database from which their algorithms can feed.