What is Deep Learning?
As most people involved in data science know, there are a multitude of areas one can focus their efforts on learning. This ranges from Natural Language Processing (NLP) to Support Vector Machines (SVMs), all the way to Deep Learning -which is what we are focusing on today. What is deep learning you may ask? I will do my best to explain it to you.
Deep learning is a class of machine learning. It works using neural networks to help it learn and “think”. These neural networks consist of many layers, thus the algorithms in deep learning can have raw data inputs. To compare, in more traditional methods of machine learning, relevant features are given to the model to train on. The goal is to mimic the human brain… rather, the human thinking process. Yes, this is the stuff that Artificial Intelligence (AI) is made of. With these new advancements in large data, deep learning is now able to explore far more complexities and observations than before.
Deep learning has helped in an abundance of areas in relation to language translations, image classification and many other forms of AI such as speech recognition.
How does all of this work exactly? As stated earlier, neural networks act as neurons for the machine. The networks consist of layers and layers of these nodes, and each node is given a signal. This signal assigns weight to the raw inputs, thus changing its effect on the nodes further down the network. Once the signals reach the final layer, the weighted inputs are compiled, and an output is produced.
It is important to note that much of these deep learning networks require huge amounts of data to perform accurately. These algorithms also require a lot of processing power. This is because the neural networks are constantly solving robust mathematical problems. In some cases, even with the most agile hardware, it can still take weeks to train using deep learning.
A Deep Learning Example
One not so common, but ever-present example of deep learning is in driverless vehicles (unassisted driving vehicles). These vehicles use algorithms that focus on image classification. As more images are fed into the network, the better the algorithm gets at recognizing differences in objects and positions. This in turn allows the model to continuously tweak it’s processing techniques, which helps with faster reactions and decision making. These algorithms are becoming highly sensitive and intuitive. The possibility of driverless cars entering into our everyday lives is very much a tangible reality.
There are many more examples of deep learning at work. To name a few, we see this in speech recognition, chatbots, virtual assistants and the list can go on. There are a lot of exciting applications of deep learning that we can look forward to for the future. With more research and discoveries being made in machine learning every day, we know deep learning will be around for a while.
References:
8 Examples of Deep Learning and Why It Matters — https://medium.com/trapica/8-examples-of-deep-learning-and-why-it-matters-854e877ab728
What is Deep Learning and How Does It Works [Explained] (simplilearn.com)