Technically, deep learning is not new. It was first introduced in 1988 when two computer scientists at MIT named Yann Lecun and Hado van Nguyen published their paper called “Convolutional Neural Networks for Visual Recognition”. Since then, it has become one of the most popular concepts in artificial intelligence (AI).
Deep neural networks have been adapted by different industries to solve increasingly complex problems. Some examples are translating text from languages that are not English, classifying pictures or videos, and solving computational geometry puzzles like finding the shortest path between two points.
There are many types of deep learning algorithms but the ones you should know about are convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM) net, and generative adversarial network (GANs). This article will focus mostly on CNN and RNN because they are some of the most common types of nets used in applications today.
However, before getting into specific implementations of these architectures, we must discuss what makes up the underpinnings of each algorithm. These components include layers, activation functions, loss functions, optimization strategies, and more.
Feedback is one of the most important tools in learning anything new. When you are practicing something, whether it be brushing your teeth or mastering computer science, you will need someone to tell you if you’re doing well and how to improve.
There are many ways to get this kind of feedback. Some people may be able to tell you directly if you look them in the eye and listen carefully. Others may suggest trying out different versions as you practice so that you can see what works and doesn’t for you.
In the case of deep learning, there are some courses and forums where people share their practices and get comments and responses from other learners. You could also join online discussion groups and talk about things you don’t understand and get answers from others.
By seeking out such conversations, you will not only learn more yourself, but you’ll make connections with other people who are interested in the same thing and can help you along your way.
Read online content for tips
Online content is one of the most valuable tools you can use in learning any skill. Content-focused education has become the norm, and it makes sense! With how quickly technology is advancing, there’s always new information to learn.
By understanding what concepts others have discussed, along with tricks and techniques they’ve shared, you can pick up some insights that may not be familiar. Plus, you get to hone your reading skills – something we all need to do if we want to remain competitive in our fields.
There are many ways to take in knowledge beyond just listening to lectures and seminars. By experimenting with different sources and formats, you’ll find one style that works best for you.
Reading through articles seems like a great way to go about it. But what if you’re already heavily invested in other educational resources? You could still benefit from looking into digital literacy programs and modes.
Here are five easy ways to read more effectively. Try out one or two each week and see which ones work for you.
Practice recording and watching yourself
A good way to practice is by practicing with recordings or videos of you doing something. For example, if you are learning how to draw, instead of just practicing your lines, try putting those lines into context by drawing some shapes or patterns.
Practicing with a more complex concept can be doing several examples at once. For instance, when teaching people how to paint using layers, they will usually teach it with a few examples on one subject then moving onto another layer. By having several layers in one lesson, you can easily use that as a practice exercise!
There are many ways to learn from others’ work so that you do not have to start completely fresh. Looking through their paintings or drawings, studying their techniques, and applying them to things you already know about is a great way to strengthen your own skills.
If you are ever struggling to apply what you have learned, looking back at past lessons may help you find the solution quickly.
Become familiar with difficult concepts
A lot of people start off learning deep learning slightly wrong by trying to dive into it too quickly. They add layers onto their networks or increase the number of neurons in each layer without checking if that is really necessary first!
This can sometimes work, but not always. You need to know when to add extra depth and when to stick with what you have before!
Practice making simple images look like super-detailed pictures and then moving onto bigger projects that are similarly complex. This will get your feet wet and prepare you for more advanced settings and conditions.
Once you’ve got some basics down, try designing your own styles or exploring other artists’ works to find new inspiration!
General tips: use Theano (or similar) as an engine to learn about neural nets, use PyTorch to learn how to create your own networks, and use either Caffe, TensorFlow, or Keras to pick which one fits you best.
Seek expert feedback
Even if you’re already very familiar with deep learning, seeking input from experts is never a bad thing. You should always strive to be better than your last effort, so looking into how others have improved can help hone your skills.
There are many ways to get quality advice for no cost or low cost. Some of the most common sources include:
Group forums and chatrooms where people discuss topics related to AI
YouTube videos that feature in-depth lessons and tutorials from experienced professionals
Blogs written by individuals who are passionate about AI
Online courses that offer both free and paid content
These resources all serve different purposes, but they all come together to make great tips and tricks for improving your knowledge. By actively engaging with the information, it will really stick!
General rules apply when listening to online talk shows and lectures: try to understand what the speaker is saying even though you may not fully agree with their points. A lot of interesting insights can be found this way.
Heed warnings too – if someone says something that makes you feel uncomfortable, walk away. If possible, look up what they said later and see why it made them nervous.
For more specific tips and tricks, read our article: 25 Ways To Improve Your Knowledge Of Neural Networks.
Try new things
A lot of people get stuck in a rut when it comes to practicing computer science. They either keep focusing on only one area, or stay within their comfort zone by learning only concepts that are already familiar to them. These are all good strategies if you want to learn computer programming!
But there is an extremely popular technique called “deep learning” which does not require much familiarity with other techniques. If you have ever heard about AI (artificial intelligence), this is what most of it is based on!
A few years ago, deep learning had barely spread beyond its initial hype. But now it has become the go-to approach for many aspiring software engineers who want to hone their skills.
There are some courses and books that teach deep learning, but they are also increasingly available as pretrained models such as neural networks.
As mentioned before, practicing deep learning is more than just watching YouTube videos or reading through tutorials online. It includes experimenting with different software, developing your skills, and asking others for their input!
Practicing deep learning isn’t easy when you first start. You have to be willing to make mistakes and learn from them. No one really teaches hard lessons the very first time around.
But don’t worry, this doesn’t mean you can’t get good quickly! In fact, it can sometimes take years to fully master something new.
It’s important to remember that no one was ever properly trained in computer science at birth. Take inspiration from those who were close to you and try to emulate what they did well.
An excellent source of information is the internet! There are many websites, blogs, and forums where people share tips and tricks for various software packages.
Reading these will help you hone your craft as a practitioner of AI.
Focus on actionable tasks
Recent developments in AI have focused almost exclusively on so-called deep learning, or neural network algorithms. But this is not the right approach for most people.
Deep learning sounds like very complex mathematics, which it is. However, what most of these applications do actually are things that require much less math. They look at lots of examples of data and learn how to imitate those patterns.
For instance, if they see many pictures with birds then they will be able to identify a bird even if you put a duck down and take away its feathers.
This is because when a computer looks at large amounts of data, it picks up on general rules and applies them to new situations. A powerful tool for understanding human behavior is psychology, and there are similar concepts in computing. When we understand how humans behave, we can apply that knowledge to make better decisions and solve problems.
That’s why it’s important to focus on practicing practical skills using neural networks instead of diving into difficult theory. There are many free educational resources available from both YouTube videos and courses on websites and apps.