Recent developments in artificial intelligence (AI) have brought us some incredible tools that require little knowledge of AI to use effectively. A popular example is what’s been dubbed as deep learning, which has seen dramatic success in applications such as computer vision and natural language processing.
In this article we will discuss how deep learning works, why it’s so powerful, and how you can begin experimenting with it yourself. We will also look at some easy ways to get started using neural networks for image classification and natural language processing.
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Blossom does digital marketing, design, and web development. She loves working on creative projects and educating others about emerging technologies. You can find her studying hard or spending time with her family at home in Singapore. Read more from Blossom here.
History of deep learning
While not technically new, there has been an explosion in interest for “deep” neural networks in the past couple years. In fact, many refer to this current era of research as the “Deep Neural Network Era!”
The term “neural network” was coined back in 1945 by German computer scientist Walter Schierenfeldt. He used it to describe what we now know as artificial neurons — complex mathematical functions that mimic neuron-to-neuron communication in our own brains.
Since then, researchers have experimented with different types of neural networks to see how well they work in solving various problems. Some use very large numbers of layers (or levels) to solve certain tasks, while others are designed specifically to tackle one task or domain.
These days, you can find applications using almost any type of neural net, from image classification to natural language processing. Companies like Google, Microsoft, Facebook, and Apple all actively research and implement these techniques into their products regularly.
Types of deep learning
Over the past few years, there have been many different types of approaches to teaching people how to use neural networks for computer vision or natural language processing (NLP). Some are more beginner friendly, while others may be closer in nature to using software that professionals use.
This article will talk about some of these different types of neural network applications and what it takes to learn them. While not every professional uses all five, they are important to know!
Types of Neural Networks
Deep neural networks can apply at several levels of abstraction. These levels include:
At the very lowest level is pixel-level classification. This applies directly to images and can identify individual objects within an image or determine if an image contains certain things like cars, water, etc.
is directily applicable to images and can identify individual objects within an image or determine if an image contains certain things like cars, water, etc. At the next highest level is object detection, which looks for instances of specific categories such as “car”, “cat”, or “dog”.
, which looks for instances of specific categories such as “car”, “cat”, or “dog”. Next up is instance segmentation, which goes one step further by breaking down identified instances into separate components (e.g.
Applying deep learning
Recent developments in artificial intelligence have brought us another exciting new tool that can be applied to various areas and industries. Developed by academics, this technology is known as deep learning.
Deep learning is an advanced machine learning algorithm that uses neural networks to learn tasks for you. What makes it different from other algorithms is how deeply it goes down into the data to find patterns.
It was first used in natural language processing (NLP) to analyze large amounts of text, then extended to vision (the way we see things like photographs and videos) and now it’s being adapted for audio (speech).
Because these applications use very large datasets, there are now many free resources available online for anyone to take advantage of the technology. You no longer need to hire expensive consultants to get started!
There are several great websites and apps that offer beginner level tutorials using pre-trained models. These will teach you the basics of what functions of the model you’re working with and how to tweak them to apply your own ideas to the task at hand.
By diving into some small examples, you’ll quickly pick up enough to create your own AI programs.
Challenges with deep learning
One of the major challenges that have prevented people from easily applying advanced neural network architectures is having access to these networks and trained models. This is no longer an issue! You can now access pre-trained, state-of-the-art neural networks for almost every task.
There are many companies that offer large libraries of pretrained models, often for a price per use. Some even allow you to use the model as much as you’d like without paying extra for a license!
Google has made their popular Inception architecture available via its Cloud Machine Learning platform. Facebook offers several different versions of theirs accessible through its AI Platform. And Microsoft recently launched one of their own called Azure Custom Vision which includes VGGNet, Resnet, and Dense nets among others.
Recommendations for learning deep learning
Recent developments in artificial intelligence have ushered in an era of what are called _deep_ neural networks. These advanced networks can learn complex patterns and algorithms from data, with very little human input needed!
Deep learning is not quite as simple as other types of AI that we’ve discussed so far, like machine learning or natural language processing. However, it is still pretty powerful and helpful to have them under your belt.
Here at The Quint, our most popular courses include several different levels of deep learning. If you’re looking to pick up new skills, then these may be worth taking a look at. You could also try searching through our course list to find something suitable depending on what level you already know.
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Deep learning isn’t hard
Now, some people may say that deep neural networks are difficult to learn, which is definitely not true!
Deep neural networks are very complex mathematical functions that require lots of data to train properly. This means that it takes longer to learn how to use them than something simpler like linear regression.
However, this doesn’t mean that you can’t get good results quickly! There are many ways to speed up the process by using pre-trained models or architectures, for example.
This article will go into more detail about different types of deep learning and how long it takes to learn each one.
Practice, practice, practice
Having fun with machine learning (ML) involves practicing different strategies and technologies for ML. These can be through doing or watching. There are many ways to learn deep learning, so choosing which one is best depends on your personal preference and what you want to achieve.
Practicing ML by doing comes in two main forms: using it on real-world projects and applications and exploring its components layer-by-layer. The first way is making use of it in various fields where it’s applied, while the second way is taking an approach that focuses more on how individual layers work together.
Both types of practices will help you develop fundamental skills such as pattern recognition, computational thinking, and software engineering. But don’t get distracted by these steps alone! A crucial part of developing any skill is focusing on why you’re practicing the technique in the first place and what you want to achieve with it.
We hope we’ve inspired you to begin experimenting with ML, whether you’re looking to improve your career opportunities or just because you enjoy solving puzzles.
Take advantage of resources
There are now many free, educational materials available for anyone to use to learn deep learning. You do not need to be rich to take full advantage of these opportunities.
There are several great sites that offer both beginner and advanced courses using various softwares such as Python, PyTorch, and/or TensorFlow. Some even have free accounts so you can experiment without spending money!
Some examples of these sites include Udemy, YouTube, and Khan Academy. By taking advantage of all the education material online, you will never feel like you are too young or inexperienced to start studying computer science.
Many universities also release very educational content online that people can access for free. Whether it is a fully-fledged lecture or just a slideshow with notes, there is always something out there for you.