Creator no programming software

According to research from Gartner , low-code application development which also encompasses no-code will make up more than 65 percent of application development activity by , with three-quarters of large enterprises using at least four low-code development tools. No-code development is also a solution to a supply-and-demand problem: a rising demand for generating more software, but a limited number of developers who can create that software.

Aside from this minimal learning curve, no-code platforms allow for faster application development, which could lead to lower costs for businesses.

It gives us the ability to solve our own problems. But perhaps the most important advantage of no-code over code is making software development more accessible. No-code development takes the power of creating software and spreads it among everyone.

Programming without code is still not a one-size-fits-all solution, though. In fact, it may even be more valued now. When it comes to the future of no-code development, Straschnov sees it as becoming a natural part of the software ecosystem, with more companies switching to no-code platforms and software engineers extending these platforms to make them more powerful.

Once I found visual development, it changed everything for me. No-code development allows others to create in a way that feels natural to them. This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light. Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition.

Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars. The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks.

Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data. While machine learning has been around a long time, deep learning has taken on a life of its own lately.

The reason for that has mostly to do with the increasing amounts of computing power that have become widely available—along with the burgeoning quantities of data that can be easily harvested and used to train neural networks. The amount of computing power at people's fingertips started growing in leaps and bounds at the turn of the millennium, when graphical processing units GPUs began to be harnessed for nongraphical calculations , a trend that has become increasingly pervasive over the past decade.

But the computing demands of deep learning have been rising even faster. This dynamic has spurred engineers to develop electronic hardware accelerators specifically targeted to deep learning, Google's Tensor Processing Unit TPU being a prime example.

Here, I will describe a very different approach to this problem—using optical processors to carry out neural-network calculations with photons instead of electrons.

To understand how optics can serve here, you need to know a little bit about how computers currently carry out neural-network calculations.

So bear with me as I outline what goes on under the hood. Almost invariably, artificial neurons are constructed using special software running on digital electronic computers of some sort. That software provides a given neuron with multiple inputs and one output. The state of each neuron depends on the weighted sum of its inputs, to which a nonlinear function, called an activation function, is applied. The result, the output of this neuron, then becomes an input for various other neurons.

For computational efficiency, these neurons are grouped into layers, with neurons connected only to neurons in adjacent layers. The benefit of arranging things that way, as opposed to allowing connections between any two neurons, is that it allows certain mathematical tricks of linear algebra to be used to speed the calculations.

While they are not the whole story, these linear-algebra calculations are the most computationally demanding part of deep learning, particularly as the size of the network grows. This is true for both training the process of determining what weights to apply to the inputs for each neuron and for inference when the neural network is providing the desired results. What are these mysterious linear-algebra calculations?

They aren't so complicated really. They involve operations on matrices , which are just rectangular arrays of numbers—spreadsheets if you will, minus the descriptive column headers you might find in a typical Excel file. This is great news because modern computer hardware has been very well optimized for matrix operations, which were the bread and butter of high-performance computing long before deep learning became popular.

The relevant matrix calculations for deep learning boil down to a large number of multiply-and-accumulate operations, whereby pairs of numbers are multiplied together and their products are added up.

Two beams whose electric fields are proportional to the numbers to be multiplied, x and y , impinge on a beam splitter blue square. The beams leaving the beam splitter shine on photodetectors ovals , which provide electrical signals proportional to these electric fields squared. Inverting one photodetector signal and adding it to the other then results in a signal proportional to the product of the two inputs. David Schneider.

Over the years, deep learning has required an ever-growing number of these multiply-and-accumulate operations. Consider LeNet , a pioneering deep neural network, designed to do image classification. In it was shown to outperform other machine techniques for recognizing handwritten letters and numerals. But by AlexNet , a neural network that crunched through about 1, times as many multiply-and-accumulate operations as LeNet, was able to recognize thousands of different types of objects in images.

Advancing from LeNet's initial success to AlexNet required almost 11 doublings of computing performance. During the 14 years that took, Moore's law provided much of that increase. The challenge has been to keep this trend going now that Moore's law is running out of steam. The usual solution is simply to throw more computing resources—along with time, money, and energy—at the problem. As a result, training today's large neural networks often has a significant environmental footprint.

One study found, for example, that training a certain deep neural network for natural-language processing produced five times the CO 2 emissions typically associated with driving an automobile over its lifetime.

Improvements in digital electronic computers allowed deep learning to blossom, to be sure. But that doesn't mean that the only way to carry out neural-network calculations is with such machines.

Decades ago, when digital computers were still relatively primitive, some engineers tackled difficult calculations using analog computers instead. As digital electronics improved, those analog computers fell by the wayside. But it may be time to pursue that strategy once again, in particular when the analog computations can be done optically. It has long been known that optical fibers can support much higher data rates than electrical wires. That's why all long-haul communication lines went optical, starting in the late s.

Since then, optical data links have replaced copper wires for shorter and shorter spans, all the way down to rack-to-rack communication in data centers. Optical data communication is faster and uses less power. Optical computing promises the same advantages. But there is a big difference between communicating data and computing with it.

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In this video, we'll take an introductory look at variables. We will learn the differences between scene, global, and object variables, as well as when to use them. The focus here is on concrete examples, so that you can leave with some real ideas of how to apply variables in your own game!

This video goes over the systems and tools that come with GDevelop to help you jumpstart the game making process. This will be useful for any game developers who are just starting out with the engine, or someone who hasn't been using all of the tools the game engine has to offer. This video goes over the object types in GDevelop, and briefly shows what each one can be used for. This will be useful for any game developers who are just starting out with the engine, or someone who doesn't understand some of the object types.

This video goes over the layout of GDevelop to show people where features are located, and briefly go over what each one does. This will be useful for anyone looking for features they can't find, or for newcomers to the engine to become familiar with GDevelop. Make a hyper-casual mobile game where the player must grab shapes and avoid bombs. Learn how to create a physics based car movement. Create a 2D platform game where the player can shoot at enemies chasing him. Create animated buttons that can be shown in your game menus main menu, selection screen, etc Imagine and publish your games with GDevelop.

Start with our tutorials and discover tons of examples inside the app. Features Games Learn Blog Download. Start making games No-code, free and super easy. Build your game with GDevelop. Try it online Download.