Two of the most talked-about subfields of artificial intelligence (AI) are machine learning and deep learning. They are not the same thing, even though they are frequently used interchangeably. Businesses and organizations looking to implement AI-based solutions need to know the difference between the two.
Usually, knowing what values you should use for the hyperparameters of a specific algorithm on a given dataset is challenging. That’s why you need to explore various strategies to tune hyperparameter values. With hyperparameter tuning, you can determine the right mix of hyperparameters that would maximize the performance of your model.
Deep learning has gained massive popularity over the last few decades. This subset of AI (Artificial Intelligence) can prove to be handy when you apply it to your business or is even a good subject to learn if you just want to increase your marketable skills. However, to reach your business or learning goals, it’s important to choose the right deep learning framework. Here, we’ll discuss and compare two popular deep learning frameworks, namely Keras and Pytorch, to help you decide which one would work the best for your machine learning projects or real-world applications.
During recent years, artificial intelligence has received tremendous attention and almost everyone is talking about it. In the field of artificial intelligence, machine learning is probably the most talked about branch from which the subset of deep learning has emerged. Deep learning is considered as the game-changer in the tech landscape. In this post, we’re going to help you understand the key elements that form a perfect deep learning guide, so that you can channel your efforts toward the right direction.
In recent times, both the terms ‘machine learning’ and ‘deep learning’ are creating a huge buzz around the AI landscape. The world is steadily becoming an artificial intelligence-first one where digital assistants together with other services act as our primary source of information. This concept is backed by the two terms we just mentioned. Both deep learning and usual machine learning are methods of teaching AI to perform tasks.
Over the past few years, you probably have observed the emergence of high-tech concepts like deep learning, as well as its adoption by some giant organizations. It’s quite natural to wonder why deep learning has become the center of the attention of business owners across the globe. In this post, we’ll take a closer look at deep learning and try to find out the key reasons behind its increasing popularity.
In recent years, artificial intelligence and big data have offered a significant number of advantages to businesses together with some new terminologies that every aspiring tech enthusiast should have a clear understanding of. Deep learning and neural networks are two such terms which are often interchangeably used by many people. But in reality, they’re not the same thing. In this post, we’re going to take a closer look at these two to help you develop a proper understanding of them.
If you’re interested in learning artificial intelligence or machine learning or deep learning to be specific and doing some research on the subject, probably you’ve come across the term “neural network” in various resources. In this post, we’re going to explore which neural network model should be the best for temporal data.