Recent developments in artificial intelligence have ushered in an era of so-called deep learning. This new technique is heavily influenced by how humans learn, with studies showing that the way children process information and connect concepts grows into understanding as they explore and research more.
Deep neural networks are computer programs designed to mimic this natural processes for figuring out patterns and relationships. By teaching the software how to recognize various shapes, sounds, pictures and text, it can be programmed to do all sorts of things like identify visual features of cars or cats or determine if something is funny or not.
There are many applications for these systems. They can work as chatbots to take over online conversations or perform image recognition algorithms to find objects, or even automate writing! All of these require extensive training data sets, though.
That’s where we come in. Here you will learn how to start building your own AI program through supervised (or “teaching”) deep learning. You will also learn about the different types of architectures used for neural nets and some basic mathematics needed to understand them.
This article will give you a strong foundation in the field and lots of resources and tutorials to get started right away.
What is deep learning?
So what are we actually learning here? If you’ve been reading about AI or machine learning, then you’ve probably come across the term “deep learning.” It seems like every article and YouTube channel uses it, but most people don’t really know what it means.
Deep learning isn’t just an advanced version of other types of Artificial Intelligence (AI) — it is its own thing!
Trained using large datasets that contain lots of examples, neural networks can learn complex patterns without being explicitly programmed to do so.
This is why many consider it to be the future of artificial intelligence. While earlier forms of AI were very good at doing specific tasks, such as answering questions by looking at pre-defined lists or teaching computers how to play games, recent developments have made it possible to create machines with true reasoning abilities.
There are some who argue that AI has already reached this level of sophistication, and that humans will soon become obsolete because of technology. This fear was popularized in the movie Her where Theodore “Ted” Stansdale becomes obsessed with his intelligent personal assistant called Samantha after she convinces him that he is in love with her.
Since then, there have been several high profile cases of companies introducing automated systems that require human intervention for successful functioning. For example, in May 2018, Amazon announced the launch of their new robotic warehouse system; workers must actively intervene to keep the robots working.
History of deep learning
With the explosion in popularity of artificial intelligence (AI) over the past few years, there are now many different types of AI being used for applications ranging from talking chatbots to autonomous vehicles. One type of AI that has seen a resurgence is called “deep learning” or sometimes referred to as neural network technology.
In fact, some experts believe that we will reach a point where computers can outperform humans in almost every field because they use concepts like neurons to learn how to perform tasks. In this way, they’re inspired by our own brains!
While not all uses of AI require complex computer programs, most do, which is why it is important to have at least a basic understanding of what deep learning is and how to implement it into your life. Luckily, you don’t need any formal education or training to get started!
This article will go through several examples of how to learn about deep learning starting with simple logarithm lessons and then moving onto more advanced material. By the end of this tutorial, you’ll know just enough about the topic to begin experimenting and adding depth to your knowledge.
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Identify what skills you need
There are many ways to learn deep learning, but most require you to have at least a bachelor’s degree or higher. This is fine if you already have an education, but it may not be accessible to everyone. Fortunately, there are several free resources available via YouTube, blogs, and other websites.
By experimenting with different concepts, strategies, and materials, you can quickly pick up some of the basics of this field. Some of the more advanced applications also allow you to contribute as a student or professional member so that you get even deeper insights into the material.
Read all of the following books
There are many great resources available for you to start learning deep learning. Many of these can be found free of cost or very cheap if you are willing to learn more about it.
There are some great blogs that contain tutorials, tips, and tricks for both beginners and advanced users. Some of the top sites with easy to follow content include:
* DataPornTrance.com – This site contains fun ways to use data in new interesting ways.
* TheArticlesTuts.com – These have a variety of helpful articles ranging from how to make your own games to how to become an artist.
* YouTube- there are several channels dedicated to teaching people different skills using machine learning and neural networks.
These websites and videos will go into detail explaining each concept so you know what is being explained!
Many universities offer courses on this topic as well. If you are already studying computer science, then adding another skill set may be easier than trying to find one independently.
Practice working through tutorials
The second way to learn deep learning is by practicing what has become known as “deep-learning theory” or, more commonly, “neural network theory.” This can be done via YouTube videos that go over specific concepts of neural networks such as convolutional nets or recurrent NNs!
There are many great sites with free content you can access to take your knowledge one step further. For example, Stanford has a very in depth course they offer here: https://classroom.stanford.edu/webxm/course/detail_topic=Neuroscience-Week2&enrollments=on. There are also several other courses from major universities and companies like MIT, Harvard, and Udemy that have separate lectures and materials for students at all levels.
By diving into this material, you will not only expand on these theories, but also get practice applying them in software applications. These apps include things such as image recognition, speech processing, and natural language understanding.
Maintain a good understanding of basic concepts
The first thing you need to do is make sure that your knowledge of other fundamental computer science topics such as algebra, geometry, trigonometry, calculus, and physics are at least close to where you want them to be!
These fundamentals play an important role in deep learning because they help you understand how numbers work and what math functions you can use when training neural networks.
For example, knowing about linear equations helps us solve problems involving adding or multiplying two numbers together. And being able to differentiate polynomials (think very complex expressions) gives you the equation for finding the derivative of a function.
You should also know what atoms and molecules look like, which really helps with the next step!
Knowing about quantum mechanics teaches you why we have limitations on using infinite sums in mathematics and gives you some insight into how computers actually work under the hood.
Practice programming with Python
Having a strong fundamentals in computer science is very important when starting off as a beginner in deep learning. Luckily, there are many free resources available online that can help you learn how to program! There are even some great introduction courses that use python as their scripting language.
Many universities now offer these kind of programs either via their website or through their digital education platform. By practicing your hand-eye coordination by doing simple projects, you will be teaching yourself how to write basic codes.
This will also give you experience using programming languages which may aid you later in your career. All professionals have at least a bachelor’s degree so this is definitely something worth looking into!
There are several different types of software engineers that do not require a bachelor’s degree but instead take advanced skills and concepts and apply them towards solving other problems.
Watch lots of YouTube videos
One of the most important things you can do to learn deep learning is to watch lots of YouTube videos. There are many great YouTubers who have very in-depth lessons using different software, technology, or strategies to teach people new skills or refresh old ones.
Many of these tutorials focus on one specific skill set like computer vision, speech recognition, natural language processing, and so on. By watching several separate lessons, you will get more in depth knowledge on each topic!
There are also some that combine different topics together into one lesson such as how to be a better photographer. Others just use the material themselves so if you find them helpful then give their channel a follow and check out what they have to offer.
General tips: remember that this advice applies not only to you but to others trying to pick up any skill or technology.