Recent developments in artificial intelligence have brought us something new: deep learning. Technically speaking, this is not AI per se, but rather an algorithm that uses neural networks to learn tasks for you.
Deep learning has become one of the most popular strategies in computer science because it can achieve impressive results without being explicitly programmed to perform a task.
Instead, it learns how to accomplish its goals by exploring data sets with computers using mathematical techniques called optimization. When it’s done, it performs the task at hand better than humans could have!
There are many areas where deep learning applies, including image recognition, natural language processing, and computational linguistics. Because these applications all depend on understanding images, text, and speech respectively, there are lots of ways to use it in the field of computer vision.
Computer vision helps machines understand what things look like and then apply those lessons to other situations. For example, if you upload a picture of your dog to a computer vision system, the software will try to identify dogs in future pictures.
By applying deep learning to computer vision, we get some pretty cool technology, such as self-driving cars and robots that can interact and communicate more naturally with people.
In this article, you’ll start off by taking a close look at why deep learning works and some of the components of neural networks before moving onto how to do deep learning in python.
The first thing you will need to do is install some key software packages used for deep learning in python. These include commonly-used applications such as PyTorch, TensorFlow, and Caffe.
There are many ways to install these apps, so be sure to check their documentation or talk with people who use them to learn how to access all of their features. Some pre-made scripts and stacks exist too!
Many universities and research labs now require students to have specific software installed before taking advanced courses like computer science or engineering. So this may also help your chances of being accepted into an advanced degree program!
General tips: make small progress every day!
It can feel very daunting starting out if you don’t know much about programming. That’s totally normal, especially early on when most tutorials seem like they’re talking way above your head.
Don’t worry though, that’s what online communities are for! Search for beginner level tutorials on YouTube, Reddit, and other sites to pick up some knowledge. (Keep in mind that some may not be quality material however.)
Overall, just start trying things out! There’s no reason to wait until everything is completely mastered – you’ll probably find there’s at least one part that you really understand well already.
Create an environment
For beginners, choosing your computer hardware is one of the most important decisions you will make when doing deep learning. Your GPU will determine how fast you can train and evaluate your neural networks!
If you have a powerful machine now, great! Keep using that equipment. But making sure your computer has enough memory to store all of the information needed for training and testing can quickly get expensive as you progress with DL.
That’s why it’s best to be selective about what kind of GPUs and CPUs you add to your collection as you go along. Buying used or online-bought computers and components can help keep up with the ever-increasing demand for trained AI models.
And don’t forget those hard drives! A lot of apps these days require a large amount of storage space to properly initialize their settings and to save the learned model. Find a balance between having plenty of room for media files and AI junk.
Image credit: tony roberts | stock photo stream| © 123rf.
Learn how to use the K-Nearest Neighbor algorithm
The k-nearest neighbor (kNN) method is one of the most straightforward algorithms for classification. It works by looking at the features of some input data and determining what kind of class it belongs to, using the labels as a reference.
The term “neighbor” refers to another example with similar characteristics to the one you are studying. In this case, your kNN will look at other examples that are labeled the same way as the one you are testing and determine which label they have, thus predicting the correct category for your test set.
There are two main components of the kNN algorithm: the distance measure and the number of neighbors.
We used the euclideyian metric – the default choice when defining distances between vectors or points – multiplied by 2 to create our distance function. This doubled the length of the equation because we were adding both positive and negative values to the result.
Now that we know the math, let’s put it into practice! To apply the kNN model on our dataset, all we need to do is define the number of neighbors and choose an appropriate value for k.
Learn how to use the Deep Neural Network algorithm
The second way to do deep learning in python is using the neural network (NN) algorithm. A NN was first proposed in 1926 by German mathematician Kurt Gödel, but it wasn’t until decades later that they became widely applicable.
A NN is basically just a set of rules or algorithms that work like neurons in your brain! This analogy really makes them seem powerful since you can create very complex patterns with these simple rules.
In fact, some experts say that we have already created several NNs through our daily lives. For example, when someone speaks, listening devices recognize the pattern of sounds together as words.
This analogy also explains why people who are highly trained in math can sometimes feel left out because they don’t understand the “math brains”. 🙂
Since NNs were originally designed for computer applications, there are many softwares and software packages that implement this algorithm. Some common examples include voice recognition apps such as Google Now, Siri, and Cortana, and image classification tools such as those used to determine what kind of animal an uploaded picture is of.
There are even some apps which apply the NN algorithm directly to human behavior data! For instance, researchers could potentially use NNs to predict if someone will commit suicide within the next week.
Learn how to use the Random Forest algorithm
The second machine learning algorithm we will look at is called random forest. This was one of the most popular algorithms for classification tasks like predicting if something is an image of a dog or not, or determining whether or not someone is likely to be involved in crime.
A random forest works by taking many different “trees” (think lots of paths) and then combining them all into one final prediction. Each tree is built using data that has been labeled either as positive or negative, with the overall result being determined by which trees are more prevalent in the individual branches.
Because there are so many components, it is difficult to know which part played what role in the decision making process. That is where statistics come in! By looking at each component individually, you can determine which ones influenced the outcome the most.
There are several types of random forests, but the one we will focus on here uses only two types of nodes. These are referred to as binary decisions, because they produce a binary response (yes/no). The first type of node produces an input variable, such as the length of a side of a rectangle. After testing this against the training set, it may decide to include or exclude the item based on whether or not it found anything resembling that size ratio within the samples it had already analyzed.
The other type of node produces a regression output, usually giving a number related to the input.
Learn how to use the Gradient Boosting Machine algorithm
The next advanced deep learning technique is gradient boosting machine (GBM). A lot of people refer to this as “gradient descent with booster shots” because it is similar to gradient descent, but instead of taking small steps towards a goal, you add onto the previous model until it gets the job done.
The main difference between these two algorithms is how they handle incorrect predictions. With gradient descent, if a model makes an error, it will learn from that mistake by being more cautious about future classes or features. This process repeats itself until the model does not make mistakes anymore!
With gradient boosting machines, after each prediction is made, there is an additional step where we can update the model. This internal layer we call a “booster�” since it adds extra layers to help correct potential errors.
By having multiple models work together, GBMs are very efficient at training. You can read more about this powerful tool here.
Practice with a dataset
The first way to do deep learning is by practicing with datasets that contain natural images or textual data. These practice sets are called pre-trained models, as they have learned how to perform specific tasks using previously gathered information!
There are many free, public pre-trained models available for you to try out. Some of the most well-known ones include: VGGNet, InceptionV3, Resnet50, and more.
By trying these networks, you can see just how much knowledge the model has absorbed during training. You can even train your own network by breaking down the structure of one of these networks.
It’s important to note that most of these pretrained models were designed to be trained on very large image datasets or text documents, not little chunks of code like we need for computer vision or language understanding.
That being said, some people have managed to get quite good results simply rewiring the layers themselves and retraining! It depends mostly upon the architecture of the given layer.
Connect with a community
Finding a good learning curve for deep learning depends heavily on your level before you start, as well as the type of learner you are. There is no one standard way to learn this technology!
There are many ways to connect with other people who are working through similar stages on their journey towards mastering AI. Online forums are a great place to meet like-minded individuals, or perhaps there’s a local event happening that everyone can attend. Either way, it’s a nice supportive environment.
Alternatively, if you already have some experience under your belt then why not join an existing team? Or maybe you know someone who is developing ML applications and would love additional help from anyone willing to share knowledge?
By connecting with others, you’ll be able to find out how they approach solving certain problems which may prove helpful.