This article will give you a deeper understanding of artificial intelligence along with two important building blocks of AI systems: machine learning and deep learning. With this knowledge and the right tools, you'll be able to harness their power to help you succeed in the financial market.
While AI research has powered a new industrial revolution over the past decades, the internet era led to the widespread availability of cheap computing sources and tons of data, making AI affordable, relevant, and profitable. When we think of AI, we often think of
In addition to the self-driving robo taxis that might handle our daily commute in the not too distant future, AI also powers the recommendation engines that keep us reading news articles, swiping through social media and
I am watching more shows on Netflix and YouTube. AI is also powering a digital transformation in the financial market, with AIs performing algorithmic trading and making data-driven decisions handling the majority of stock trading activities in the United States. of these are generally powered by some kind of machine learning algorithm. While there are many different kinds of machine learning algorithms, they all share the same core process. Now understand that this training data can be from the real world, artificially generated, or even stuff that you make up on the spot. That's important to note because it means that having bad data in your training set can negatively affect the accuracy of the model. You might have heard that in order to improve a model you just need more data, but really you need better data. As grammar sticklers would say, better data Machine learning algorithms can certainly be very complicated, but they can also be as simple as the things you learned in your first algebra class. Let's produce a machine learning model to predict future stock prices of Many of you are probably familiar with linear regression. Even if you don't remember how exactly the algorithm turns out, y equals mx plus b. You probably know how to run the algorithm and the line it produces is the model. These three models are different but
They were created using the same algorithm, but with a different training data set. How good are these models usually? You've been taught to test the model against its own data, which produces an r or r-square value, but you're here to predict the future, so the real test is against data it's never seen before. Here are spy stock prices one month after the training data set ended.'s a non-exhaustive list of machine learning algorithms all making different assumptions about the fundamental behavior of your data. As you would expect, algorithms that more closely represent the true behavior of the data will produce more accurate models. And that's a key thing to keep in mind about machine learning. You can run any algorithm on any data set and get a model. This data is clearly quadratic, but we can still run the linear regression machine learning algorithm and produce a model. It just might not be useful, but you might consider it at least a bit more useful compared to a model that just outputs one number or maybe not. Some people would argue that there's a better way to represent the data.
While linear regression is a less complex machine learning algorithm, you can draw many parallels to advanced algorithms. Fundamentally, you run the algorithm on a training set of data to produce the model and verify its accuracy against new data. Each entry in the training set gives the model the same set of inputs as the correct answers in this simple case. date and stock price. More complex algorithms will have multiple inputs and might even have multiple outputs and sometimes the prediction isn't a number, it could be a category like a model that receives an image and identifies a person. Speaking of identifying a person in an image Deep learning is a subset of machine learning that is often used in these sorts of recognition tasks. Deep learning algorithms imitate the workings of the human brain and the models they produce tend to look like a network of neurons in the brain, so they are called deep neural networks. What's the difference between deep learning and machine learning? In machine learning, you're the one who tells the algorithm what's important and what features to look at. In our previous example, there was only one date. While date is actually somewhat useful since company revenue can be tied to certain seasons, clearly there's more to stock prices Alpha vantage tools make it easy to access hundreds of different features and
The big question is which of these features is actually important. The main offering of deep learning is that deep learning algorithms figure out what features are contained in the data and implicitly which ones contribute best to accurate predictions. In other words, these algorithms learn directly from the data. Take face recognition as an example. In order to use machine learning, you would need to define yourself how to detect elements on a face. How you define the shape of the eyes, nose, cheekbones, mouth, hair, ears and their individual relative positions to one another and remember that computers don't see pictures, pictures are just numbers to a computer, so have fun translating that. If that sounds like a lot of work, it is and you'll appreciate that deep learning algorithms figure out those features themselves even if their understanding isn't quite the same as human intuition. The difference with deep learning is that you need lots and lots of data to improve model accuracy. Machine learning might entail a lot of work up front to
Even with a small training data set, you can get accurate models, but they both work the same way. based on training sets of data where each entry contains the same set of inputs along with the correct answer, and some algorithms are more useful than others for a given task on the topic of usefulness. How good is good enough? While models for the real world rarely give accuracy because the real world just doesn't work that way. It often takes a lot of effort to increase a model's accuracy from the initial results of the algorithm. While you are encouraged to look up ways to fine-tune your model, just keep two things in mind. 1. Train your model on more good data and, ultimately, you will be the one who decides what is good enough. So when it comes to predicting stock prices, you'll have to decide what constitutes good enough, but keep this in mind: if a model is accurate, whatever that might mean, if it predicted winning lotto numbers, you'd be set for life pretty soon, but if it was an AI on a self-driving car that needed to differentiate a crosswalk sign from real people, that would be terrible. So when it comes to predicting stock prices, you'll Deep learning requires lots of good real-world data. While there is still work to be done, hopefully you have a better understanding of what AI, machine learning, and deep learning entail, as well as an appreciation for what deep learning can offer.
Especially with all the data available in the financial industry. For examples related to the financial market, checkout Alpha Academy's tutorial on using the LSTM deep learningan algorithm to predict stock price movements.
Alpha Vantage Coin YT Channel : https://www.youtube.com/channel/UC_XhdS9N4kobpL6XAA7JSJw
(