Top Deep Learning Myths You should know

This post highlights the top deep learning myths you should know. This is important to understand in order to leverage deep learning to solve complex AI problems. Many times, beginner to intermediate level machine learning enthusiasts don’t consider deep learning based on the myths discussed in this post.

Without further ado, let’s look at the topmost and most common deep learning myths:

• Good understanding of complex mathematical concepts: Well, that is just a myth. At times, they say that one needs to have a higher degree in Mathematics & statistics. That is not true. With tools and programming languages along with libraries available today, basic mathematical concepts should be able to help you navigate through using deep learning to solve complex problems
• Great volume of data must be available: This is also a great myth that you need a great volume of data, preferably in GBs to train deep learning models. This is also related to the “Volume” criteria of “Big Data”. That is incorrect. There have been breakthroughs with data of size as less as 100 records or so. In other words, data in MBs can also suffice the need for training good deep learning models.
• Expensive computers of large configurations (GPUs) are a must: That is not true. You could train deep learning models by procuring costly computers of large sizes.

Given the above, you can get started with deep learning in no time by using some of the following. There are the most popular frameworks which can be used to get started with training your deep learning models. 

• Tensorflow
• Pytorch
• Keras

You can get started with deep learning by going on one of the following platforms:

• Google Colab
• Kaggle