Special Offer - Enroll Now and Get 2 Course at ₹25000/- Only Explore Now!

All Courses
What is Deep Learning?

What is Deep Learning?

November 21st, 2018

What is Deep Learning?

Getting Started With Deep Learning

The whole world is buzzing about artificial intelligence and its applications. When you hear about any latest AI based innovation, it is highly probable that you are listening about the successful execution of Deep Learning principles. Yes! Deep learning has strengthened AI significantly and is racing it ahead towards an increased number of innovations.
If you are a newbie to the term ‘Deep Learning’, this beginner guide is for you. Dig ahead to find out how to start your journey with Deep Learning.

Deep Learning – Simplified

Deep learning is a specialized set of machine learning methods that finds its roots in the communication and processing mechanisms of biological nervous systems. It is a set of methods that can train It differs from other branches of AI in the sense that it is based on learning data representations, while the others are task-specific algorithms.

What can Deep Learning do?

Deep learning has vast potential to boost the performance of AI -based creations such as speech recognition, language processing, computer and board game programs, image restoration, financial fraud detection, medical imaging and other robotic based applications. Amazingly, the output in all these applications were far ahead of those compared to when performed by humans.

“What’s behind the driverless cars? Artificial Intelligence, or more specifically Deep Learning.” ~Dave Waters

Following are some of the huge hit applications that were designed using deep learning:

  • M, Facebook’s AI-driven virtual assistant
  • RankBrain, Google’s deep learning based system to filter search results
  • Well established virtual assistants – Siri or Google Now


Now that we got an idea about what Deep Learning is all about, let us discuss what are the basic requirements a beginner is expected to know to begin the journey with Deep Learning.

  • Intermediate to advanced knowledge of linear algebra, calculus and statistics
  • Hands-on expertise in programming
  • Strong conceptual knowledge of machine learning. The prerequisites for applying it are just learning how to deploy a model.

You may choose any online resources or tutorials to gain basic understanding of Math, Statistics and Machine Learning.
Talking about programming requirements, as a beginner you are expected to have a basic knowledge of Python and Conda environment.
If you are planning to learn Deeplearning4j, you should know Java too.
To reach a confident level in the basics, it normally takes 2-4 months for a beginner.

Master Deep Learning in various Stages

Learn the basics of Deep Learning from a reputed text book or video tutorial or attending a course, whichever mode suits you
Gather a strong idea and usage of various key libraries of deep learning, such as Tensor Flow, Keras, Lasagne, Torch and DeepLearning4j.
Later you will have to delve into deeper concepts of Deep Learning, such as NLP, Computer Vision, Speech and Audio recognition, Reinforcement Learning, etc. We suggest you pick each of these by starting from their basics to working on small projects and then going to advanced levels.
Once you are sure that you can conquer small projects based on these topics, now is the time to explore and test your Deep Learning skills by building new and challenging projects of your own.
Research papers and updates from national and international seminars and conferences will help you to stay abreast of latest advancements in Deep Learning.

Job Prospects

Reports from recruiting portals show a steep rise in the requirement for Deep Learning professionals across the globe. In the USA alone, by 2024, a whopping 2,50,000 jobs are estimated in this specialization. Besides, the top 20 AI companies are ready to spend $650 millions for their upcoming AI projects and hiring new AI professionals.
Hope our step by step guide will be helpful to begin your Deep Learning experience. Any questions or comments, please drop them here.