6 Reasons Why Your AI And Data Science Projects Fail


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4. Resource Constraints:

The worst part about a brilliant idea which you know you can make it work but unfortunately you don’t have the right resources available to you.

For numerous complex Data Science projects, you will either need a large number of data requirements for a collection of large datasets for machine learning tasks.

Or you might require more computational resources such as RAM size or Graphics Processing Units (GPUs) to solve specific tasks on deep learning.

While there are several free resources available for your to construct beginner to intermediate level projects, including cloud platforms, they are rarely sufficient for high-level, complex applications.

Something like a Google Colab could work decently well for intermediate applications, but the time and resource constraints could cause several issues.

When you trying to construct a specific project on something more complex like a project on Generative Adversarial Networks (GANs) and you need to work with high-resolution images, the resource constraints are a huge issue.

Without proper computational resources, you will always receive a resource exhausted error, or you simply can’t execute your programs.

The potential fixes for these issues are to reduce the image quality and the number of layers in your neural network. While the results might not be as desired, you will end up with some decent results to verify your methodologies and algorithms.

For a more in-depth understanding of graphics processing units and their precise requirement for deep learning, I would highly recommend checking out one of my previous articles on do you really need a GPU for Deep Learning.

If you are a beginner who is just getting started with the development of deep learning projects and you don’t know the best type of architecture for your PC builds, I would recommend checking out one of my previous articles on the best PC builds for deep learning in every single budget range.


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