Deep learning is a sub-set of Machine Learning that is based on Artificial Intelligence (AI) methodologies. It mimics the human brain functionalities concerning processing data. There are various layers through which data is processed, hence the word ‘deep’.

The deep learning algorithms or deep learning techniques are inspired by the human brain’s structure and functioning and aim to make machines intelligent enough to do complex tasks. Deep learning techniques create models with several hidden layers of neural networks to make accurate predictions. Some of the deep learning applications are self-driving cars, virtual assistants, fraud news detection, automated game playing, visual recognition,
language translation and many more.

There are different types of deep learning techniques, each used for the other purpose. Discussed below are the top 7 deep learning techniques:
1. Transfer Learning: It is the process of improving a previously trained machine or model to perform new and more specific tasks. This technique is beneficial as it needs a very less amount of data than other techniques and helps reduce large computation time.
2. Boltzmann Machines: There is no predefined direction of this model. This deep learning technique is used for system monitoring, binary recommendation platform, and specific dataset analysis. Its nodes are arranged circularly, and it is a unique deep learning technique used in making model parameters. It is pretty different from other network models and is also known as stochastic.
3. Deep Reinforcement Learning: This deep learning algorithm has an input layer, an output layer and other multiple layers that are hidden. This model aims at predicting the future reward depending on the input actions. This deep learning technique is meant for board games, self-driving cars, robotics and others.
4. Back-Propagation: This technique was launched in 1970. It is also known as backward propagation. It is a supervised deep-learning algorithm that is used to train artificial neural networks. This method uses a technique called gradient descent.
5. Batch Normalisation: It is supposed to be one of the vital parts of preparing data for deep learning techniques. It was launched in the year 2015 and is one of the latest methods of deep learning. This technique is used for improving the performance as well as the stability of an artificial neural network. It also helps in reducing the training period required to train deep neural networks.
6. Stochastic Gradient Descent: Stochastic Gradient Method or Stochastic Gradient Descent or SGD is also named as Robbins Monro Method because it was invented by an American mathematician statistician, Herbert Robbins, in 1951. It is also one of the core techniques behind the AI revolution, and supposedly, it is also among the most widely used deep learning techniques used by AI enthusiasts. This technique uses a few randomly selected samples instead of the entire dataset for each iteration.
7. ResNet: In 2015, Kaiming He designed ResNet, also known as Residual Neural Network. Deeper neural networks are much more complex and challenging to train, and this is where ResNet steps in. It is used to improve deeper neural networks and break down the deep neural networks into smaller networks. These are further connected using either skip or other shortcut connections to make a larger network.

There are different types of deep learning techniques, each used for the other purpose. Discussed below are the top 7 deep learning techniques:

1. Transfer Learning:

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2. Boltzmann Machines:

3. Deep Reinforcement Learning:

4. Back-Propagation:

5. Batch Normalisation:

6. Stochastic Gradient Descent:

7. ResNet:

Growth of Deep Learning & AI Technology and Demand for Skilled Professionals
The AI industry’s revenue in India grew from $ 230 million in 2018 to $ 415 million in 2019. The number of professionals working in AI in India also increased from 40,000 in 2018 to 72,000 in 2019 (Livemint).
Deep learning and artificial intelligence are being widely adopted in India in various sectors. According to an article in India Today, as of 2020, there was a 30% annual increase in the number of companies setting up dedicated AI teams and 68% of the Indian firm were set to adopt AI.

Growth of Deep Learning & AI Technology and Demand for Skilled Professionals

In 2020, an AI specialist’s job stood second in the list of emerging jobs in India. The four cities of India with the most number of AI professionals are Bengaluru, Delhi, Mumbai and Hyderabad.

This is just the beginning as an increasing number of businesses adopt AI, so many jobs are being generated in this field. Consequently, the demand for AI professionals is increasing in India at a very high rate. According to Gartner, the AI industry will provide 2.3 million more job opportunities in 2020 globally. Hence, a large number of deep learning and AI courses are also being launched by institutions.

Why Choose IIT Ropar’s Post Graduate Certificate in AI and Deep Learning?
With the rising demand for deep learning and AI courses in India, many universities are coming forward with uniquely designed courses in this niche. The Post Graduate Certificate in AI and Deep Learning by IIT Ropar, in collaboration with Times TSW, is currently one of the dynamic courses currently available. To enrol in this deep learning and AI course, one needs a minimum of 2 years of professional experience in the IT industry. Still, coding and mathematics background is not compulsory. Students will be provided with a joint certificate from TSW and IIT Ropar. It is one of the top IIT PG Courses that can change your career path and take you to new heights of success. Learn more about Times TSW & IIT Ropar’s Deep Learning and AI Course today!

Why Choose IIT Ropar’s Post Graduate Certificate in AI and Deep Learning?

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