With the rise in technological advancements in the field of artificial neural networks, there have been several libraries that are used to solve and compute modern deep learning tasks. In my previous articles, I have covered some other deep learning frameworks, such as TensorFlow and Keras, in detail. It is recommended that the viewers who are new to this topic to out the following link for TensorFlow and this particular link for Keras. In this article, we will cover another spectacular deep learning framework in PyTorch, which is also widely used for performing a variety of complex tasks. PyTorch, since its release in September 2016, has always offered stiff competition to TensorFlow due to its Pythonic style of coding archetypes and comparatively more simple coding methodologies in some cases. The table of contents for the concepts we will discuss in this article is provided on the right. For starters, we will get accustomed to PyTorch with a basic introduction.

Every time when I tell my friends that I study Artificial Intelligence and specialise in Deep Learning methods, they often end up with an amazed expression. Probably they're thinking: 'Wow this guy is a genius…'; or probably they just see me as another Computer Science nerdy guy. Many of you might have a sense that'AI', 'Machine Learning', 'Deep Learning' are concepts that could be understood with extremely strong mathematical background and programming knowledge -- and it is true if you really wish to master this field and become an industry leader like my idol Ian Goodfellow, who now works for Apple as a Machine Learning Director. However, what I notice is that many of you might actually want to know how'all those magic' works in AI, in a casual sense from an outsider's point of view. If you have been searching for articles about different concepts in AI, you would notice that a lot of which are enigmatic for beginners or'outsiders' -- full of jargons, sophisticated clauses and mathematical expressions, which definitely aren't the answer you're searching for.

As a geospatial professional in 2020, it's nearly impossible to avoid being exposed to the ideas of AI, machine learning, and deep learning. Projects utilizing these technologies have been at the forefront of articles, panels, and presentations -- often displayed proudly on stage during conferences and other events (virtual or otherwise). What is interesting to me is that, despite their recent popularity, there are still many misconceptions about what these words really mean, and how they can be applied within a geospatial context. "You mean they aren't just all synonyms for the same thing?" Let's help G. O. out a bit, and go over the differences between the terms here, so that they can be prepared to dip their own toes into the GeoAI waters.

In this article we are going to study in depth how the process for developing a machine learning model is done. There will be a lot of concepts explained and we will reserve others, that are more specific, to future articles. The first, and one of the most critical things to do, is to find out what are the inputs and the expected outputs. It is crucial to keep in mind that machine learning can only be used to memorize patterns that are present in the training data, so we can only recognize what we have seen before. When using Machine Learning we are making the assumption that the future will behave like the past, and this isn't always true.

In this article we are going to study in depth how the process for developing a machine learning model is done. There will be a lot of concepts explained and we will reserve others that are more specific to future articles. The first, and one of the most critical things to do, is to find out what are the inputs and the expected outputs. It is crucial to keep in mind that machine learning can only be used to memorize patterns that are present in the training data, so we can only recognize what we have seen before. When using Machine Learning we are making the assumption that the future will behave like the past, and this isn't always true.

Artificial intelligence (AI) is advancing rapidly, thanks to ever-more-powerful computing, massive growth in the availability of digital data and increasingly sophisticated algorithms. The world's largest technology firms are investing billions to develop their AI capabilities, and companies across industries, from travel to real estate to fashion, are racing to bring AI-enabled services to market. "The world's largest technology firms are investing billions to develop their AI capabilities." AI has the potential to bring significant social benefits, including healthcare (via improved diagnostics), transportation (through self-driving vehicles) and law enforcement (with improved fraud detection). AI also brings new social risks, including discrimination (from algorithmic bias), privacy (through the misuse of personal information), child rights (through lack of informed consent) and labor rights (because of the mass displacement of workers by machines).

In a recent article, we demystified some of the technical jargon that's being thrown around these days like "artificial intelligence", "SaaS, "the cloud", and "deep learning". While the techies can debate among themselves the difference between "machine learning" and "deep learning", we're going to consider the two terms synonymous and henceforth just talk about "deep learning". So just what is "deep learning"? We wanted to understand more, so we came across this excellent TED talk given by Jeremy Howard which finally explains in layman's terms just what deep learning is. If you have 20 minutes, watch the video now and no need to read any further. If you don't have time to watch the video, here's what we learned. When you use Google Images to search for a "grey cat", Google Images shows you grey cats. Is this because Google can recognize what a grey cat looks like? This is simply because Google searches text to find grey cat images. So how can we train Google to identify grey cats by only looking at images? Here's how we do it. Let's start with a sample of 10 million random pictures from Facebook and teach Google how to learn. The first part entails scanning this massive set of pictures using an algorithm developed by a software developer at Google. What does this algorithm do? It looks at the relationships of pixels in a digital photo and tries to find objects of a similar shape. Let's try this with a simple example. Let's say the pictures were black and white and composed of circles, triangles and squares. You could quite easily imagine an algorithm that could first identify the differences in color (every color is actually a unique code in software) and then start to map sharp differences in color that would denote shapes. The shapes could then be described by the direction of the lines as either circles, triangles, or squares. You could even go ahead and make them color pictures. The computer can now point out a "red triangle" or even a "beige circle". Without even having to do much coding, the computer now has the intelligence of a small child when it comes to identifying shapes. Now let's take this to the next level. Let's take a sophisticated deep learning algorithm and feed it 100 million pictures from Facebook. Let's tell the algorithm to try and find similar objects in this "big data" set and then group them. These groups are displayed to a developer who can then label them. Humans would perhaps be the most obvious and frequent object that the computer would identify. The developer would then be shown 50 humans the computer identified and could start to label sets within the group like "old person", "baby, "Chinese person" or "freckled person".