Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur within 3 years.

-Mark Cuban

Although Mark Cuban’s statement may seem outrageous yet message is dead on! We are in the middle of a revolution – a revolution caused by huge data and a tonne of computational power. Consider for a moment how someone in the early 20th century may have felt if one isn’t conversant with electricity. You might have spent years becoming used to doing things a certain way before things started shifting all of a sudden. Deep learning is essentially on a similar trajectory.

Deep Learning-What is it?

As artificial neural networks will mimic the human brain, deep learning is also a type of mimic of the human brain. Deep learning is a subfield of machine learning that is entirely based on neural networks. We don’t have to explicitly programme everything in deep learning. Deep learning is not a brand-new notion. It has been in existence for a while. It is ubiquitous now because we have access to more data and computing capacity than we did in the past. Deep learning and machine learning emerged as a result of the exponential rise in processing power over the past 20 years and Developers may soon find themselves behind the curve and required to undergo extensive training if deep learning technology research continues at the current rate.

The Potential of Deep Learning

Deep learning is fuelled by huge piles of information which is a major benefit and a significant factor in understanding why it’s growing popular. Many new deep learning improvements will be possible in the “Big Data Era” of technology. The most crucial factor to keep in mind is accuracy if you’re wondering why deep learning will be around for the long haul. Because technology is constantly improving, deep learning today has a higher recognition accuracy than it has ever had. Every time, consumer electronics can satisfy the user’s needs. It is essential for products like autonomous cars to adhere to safety standards and serve their intended function on the road. In order to be able to get value from data sets, a deep learning aspirant needs to have a thorough understanding of data science ideas and methodologies. They need to be proficient in a number of crucial data science abilities, such as prioritising tasks and knowing how to quickly locate and sort specific data within a set.

Technitute’s Initiative on Deep Learning

The Deep Learning Specialization offers a chance for aspirants to advance their career by assisting them in acquiring the information and abilities needed to take the final step in the field of AI thence Technitute is now geared up to Introduce the aspirants to Deep Learning as well as Perceptron Learning Algorithm, Neural Network Basics, Shallow Neural Networks, Deep Neural Networks, Introduction to TensorFlow, TensorFlow Deep Drive, Keras and Deep Learning Libraries, Understanding Back Propagation, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Object Detection and Deploying a Sentiment Analysis Model / Different Deep Learning Model.

Under the supervision of Professionals, Aspirants will get to know the capabilities, challenges, and consequences of deep learning. Buoyantly, they’ll labor themselves to participate in the development of leading-edge technology.