Artificial Intelligence and machine learning are two words that are making waves in the entire tech world of today. You can sense the impact that AI has on our lives, starting from the voice assistant and ending with predictive algorithms (like YouTube, Netflix, Amazon, etc.).
Everything is powered either by effective machine learning or an Artificial Intelligence project. It is predicted that these industries would only grow in the upcoming years. Especially if consider the things which it could allow us, humans, to do like self-driving vehicles, Self-flying Aircraft and could even make the entire shipping industry devoid of land-based transportation (using AI-powered flying drones instead)
Artificial intelligence is the study sector that allows the machine to exhibit human-like intelligence and prowess of doing tasks that traditionally, only a human being can do. However, considering the state at which machine learning and artificial intelligence are in the current paradigm, this definition is certainly misleading.
Yes, it is the field of study in which we try to make machines learn as fast and as good as a human can, but the creation of a completely autonomous Artificial Intelligence is a thing of the future. The same thing applies to machine learning, as well. Although it is true that we are lightyears ahead of what we were about two decades ago, but we still have a long journey to go.
If you have decided to be a part of this journey and stick with Artificial Intelligence for the rest of your life, chances are you have to either learn Python, R, or MATLAB (and possibly even all the three.)
Yes, even though most of us start our coding journey with C++, we are very quick to discard it whenever we are doing any task outside of our academics. Perhaps pointers or the janky syntax gets the better of us, or maybe it is something else?
Whatever may be the case, choosing python over C++ has some apparent benefits. For once, the syntax of python is straightforward to understand and write. Moreover, we also get most of the code written for us in the form of code libraries.
However, the converse is also true. There are some obvious benefits of choosing C++ over Python as well. Firstly, the speed which C++ offers is unmatched by any other programming language in existence. Secondly, C++ is an ancient language and meaning you would have a broad community backing you whenever you get stuck in a rut.
Many skilled developers dedicate a significant chunk of time to helping their fellow mates out. Not just that, there are many readily available libraries in C++ as well, which would make your development a much smoother experience.
To be able to take proper benefit of the speed which C++ allows, you must have a certain level of prowess with it. One of the better ways of gaining just that would be trying out some really amazing artificial intelligence projects in c++.
There are many projects available out there for you to choose from. The presence of such a variety often makes people scratch their heads. That is why we have listed some great open-source artificial intelligence project ideas in c++ down below in which you would be able to contribute.
Before we begin, it is important to note that most of the libraries in python often are wrapped under C++, only like TensorFlow. So, you should not be surprised when you see some familiar names on this list.
So, in no particular order, let us begin our discussion of the best artificial intelligence project ideas in c++
Best Artificial Intelligence Project Ideas in C++
Caffe or also known as Convolutional Architecture for Fast Feature Embedding, is going to be our very first pick in this list. It is an open-source project based on Artificial Intelligence and a deep learning framework created at the house of BAIR (Berkeley AI research Center).
The main code or the code which would be responsible for running the entire show is actually written in C++ and then served as a python library. Caffe is a tool that was created with computer vision in mind. Mainly because of the speed, the fact that it is modular, and its overall expressiveness. Now we can easily see Caffe being used in large scale applications. The main set of features which makes it stand out from the others are listed below:
a. The overall design of Caffe was created, keeping its expressiveness in mind. Because of such nature of this project, you can use it to churn out a considerable amount of data. It also encourages developers to think outside of the box.
b. You have the power of switching from a CPU to a GPU whenever you want without tanking your performance significantly. It is generally the case at all the Artificial Intelligence projects run much better when they are used on a GPU instead of a CPU. Since powerful GPUs are very costly and are rarely available for use, the fact that you can use your CPU and get almost the same performance is a terrific boost.
c. If you happen to use a GPU, however, you would get better performance. If you happen to use the NVIDIA K40 GPU, you would be able to process over 60 million images in a single day.
d. Caffe is speedy. This makes it one of the best tools to be used in research.
There is no way we when we are talking about artificial intelligence projects in c++ we do not talk about the giant TensorFlow. It is created by the team at Google and made it so that anyone would be able to see what is happening under the hood.
It is one of the best frameworks that one can use to do any task related to deep learning. With the addition of TF 2.0, it now comes with a version of Keras already installed and ready to use. Keras allows the deep learning models to be created in just a few lines of code with the help of their sequential API.
TensorFlow has one of the most flexible architectures that we have seen in some time. You can switch between using CPUs and GPUs with the help of just a single API call.
Like Caffe, even TensorFlow is powered under the hood by C++ and is wrapped over by a python layer and then served as a python library. However, there are other languages for which you would be able to find this TensorFlow library.
Twitter, Dropbox, eBay, Intel, and many other companies have already shifted to using TensorFlow for their daily applications. Some salient features make TensorFlow great. We have listed some of them below:
a. If you are using TensorFlow, you get some really good features bundled in with it. You get access to something which is known as a Tensor board. With the help of a tensor board, you can easily see how your model performs graphically. You can easily check and change the code wherever you find it necessary.
b. TensorFlow happens to be a very flexible library, which is also very modular. You have the power to chose the parts which you want to include if you are making it standalone.
c. With the help of the LSTM (long short term memory) model of TensorFlow, you would be able to create responses to the emails without you having to do anything.
d. It uses something which is known as feature columns. It is used to work between the data, which is unprocessed, and the estimators, which can be used to send or receive signals to your model.
These projects encompass the popular applications of programming. While all these projects require different tools under the umbrella of programming, they all have some things in common. Executing programming project ideas require willpower, perseverance, and a thirst for knowledge.
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