Both MXNet and TensorFlow are open-source Deep Learning frameworks. Deep Learning frameworks are essential for businesses to understand their customers better. However, if you’ve never had to choose one before, it can be difficult to choose between them.
In this article, we’ll explain what MXNet and TensorFlow are. To help you choose between them, we’ll weigh the benefits and disadvantages of each framework. Hopefully, with our help, you’ll find the best Deep Learning framework.
What Is MXNet?
MXNet is an open-source Deep Learning framework that is utilized by a wide range of businesses. It was founded by Apache and supported by Amazon Web Services and was developed to combine several different programming approaches into one major approach.
Using it, you’ll find that it can fit small amounts of memory to deploy to smaller systems. This makes it suitable for anyone using smartphones. While it has a small open-source community, it’s still used by a range of developers for a wide range of applications.
Benefits Of MXNet
One of the major benefits of MXNet is that it’s fast, even faster than TensorFlow. It offers more scalability for your data models and provides efficient data. As all major platforms support it, you don’t need a specific computer to run it.
Not only does it support major platforms, but it also provides GPU support, with a multi-GPU mode also available. Alongside its efficiency, we’ve found that it supports a wider range of programming languages than Python. This makes it easier for new users to adapt to it.
All models are easily served, and they’re simple to understand. Combined with the high-performance API, we find that it’s easy to use, allowing more versatility. Weighing these benefits, we can understand why MXNet is so widely used by a range of organizations within the tech industry.
Disadvantages Of MXNet
With all frameworks, we’ve found that there are always disadvantages to consider too. One major disadvantage is that MXNet isn’t as popular as TensorFlow. Due to this, there’s a smaller open-source community, as the community goes to other frameworks instead.
While a smaller community may not be an issue for some, open-source programs rely on their community. As MXNet has a smaller community, you may find that tech issues and updates take longer.
There are fewer people working on MXNet, so if you find a significant issue with MXNet, it will take longer for any issues to be resolved. Otherwise, it’s a good program to use for any user.
What Is TensorFlow?
If you’ve been looking at Deep Learning frameworks for a while, you will notice that TensorFlow’s name has appeared a lot. They’re the most famous open-source Deep Learning library, and a wide range of businesses use it.
As a popular tool in the industry, you’ll find that it powers a number of useful applications that you use in your everyday life. TensorFlow originated as a Deep Learning tool, so it was designed for this purpose. It was a Deep Learning tool before it was made open-source by Google.
As Google was the originator behind TensorFlow, they have a large supportive network, which is partially why it is so popular.
Benefits Of TensorFlow
TensorFlow has a wide range of benefits. One of its biggest benefits is that it’s incredibly user-friendly. As long as you have the experience, you won’t have any issues with the usability of the program.
TensorFlow is best for anyone who wants to see their models at work, as you’ll be able to view them using the TensorBoard. The TensorBoard is a tool used to monitor and visualize your models as you work. Over time, TensorFlow has branched out, so you can use it via your smartphone too.
Most importantly, due to TensorFlow’s popularity, you’ll find that there is wider community support available to you. Included in the community support are developers from Google, so if you have any issues with the program, you can contact someone quickly for help.
Disadvantages Of TensorFlow
While TensorFlow is user-friendly, it is not known for being beginner-friendly. You need to know Python programming to get the best results. As it uses neural networks, you also need to have knowledge of how they work too.
Due to these issues, you need to do your research and gain experience before you can start using TensorFlow. The reason you need to know Python is that it’s the only full programming language used by TensorFlow.
It may support other languages but doesn’t offer the same level of support as Python. If you’re writing your own program, you may be frustrated to find that OpenCL and TensorFlow aren’t compatible.
This can be a significant issue when you’re writing your own data, so you should keep this in mind when using TensorFlow. We’ve also noticed that TensorFlow is a very ambitious program, but because of that, there are issues that we’ve found.
When you generate models, you can generate multiple models at once. However, as they’re generated at once, there have been considerable issues with lag. Due to the lag issues, TensorFlow is actually slower than MXNet.
Not only is lag an issue from this, but if you’re having issues with your data, you may find that it’s more difficult to troubleshoot them.
What Is Best For You?
Now that you have a better understanding of MXNet and TensorFlow, we hope that we’ve supplied the resources to help you make an informed decision. If you’re still struggling with your final decision, we can break down the main issues you may face when choosing between these programs.
We find that MXNet is better for beginners who want to use Deep Learning frameworks, as it’s a lot more versatile. You don’t need to know a specific programming language to use it, and it’s also more scalable.
As it’s so popular in the tech industry, you should have no issue finding support when needed. While it may take time to get updates, getting answers to your questions shouldn’t be an issue. TensorFlow is the most popular Deep Learning framework used in the industry.
However, it does have a steeper learning curve. It’s not beginner-friendly, so it may be a more suitable option for you if you already have experience using Deep Learning frameworks. We also recommend checking that your computer can run TensorFlow, as it can be an intensive program to use.
Overall, if you still can’t decide which Deep Learning framework to use, you could always start with MXNet and move on to TensorFlow as you get more experience. Ultimately, the decision falls with you. Everyone has different reasons for using Deep Learning, so find the best choice for you.