Deep Learning has become one of the primary research areas in developing intelligent machines. Most of the well-known applications (such as Speech Recognition, Image Processing and NLP) of AI are driven by Deep Learning. Deep Learning algorithms mimic human brains using artificial neural networks and progressively learn to accurately solve a given problem. But there are significant challenges in Deep Learning systems which we have to look out for.
In the words of Andrew Ng, one of the most prominent names in Deep Learning:
“I believe Deep Learning is our best shot at progress towards real AI.”
If you look around, you might realize the power of the above statement by Andrew. From Siris and Cortanas to Google Photos, from Grammarly to Spotify’s music recommendations are all powered by Deep Learning. These are just a few examples of how deep in our life Deep Learning has come.
But, with great technological advances comes complex difficulties and hurdles. In this post, we shall discuss prominent challenges in Deep Learning.