Seminar Topics on Deep Learning

Deep learning - Seminar topics

Deep learning (also known as deep structured learning or differential programming) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.

Types of Data

Deep learning can be applied to any data type. The data types you work with, and the data you gather, will depend on the problem you’re trying to solve.

Sound (Voice Recognition)
Text (Classifying Reviews)
Images (Computer Vision)
Time Series (Sensor Data, Web Activity)
Video (Motion Detection)

What is deep learning?

The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without human involvement. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve.

Benefits or advantages of Deep Learning

• Features are automatically deduced and optimally tuned for desired outcome. Features are not required to be extracted ahead of time. This avoids time consuming machine learning techniques.
• Robustness to natural variations in the data is automatically learned.
• The same neural network based approach can be applied to many different applications and data types.
• Massive parallel computations can be performed using GPUs and are scalable for large volumes of data. Moreover it delivers better performance results when amount of data are huge.
• The deep learning architecture is flexible to be adapted to new problems in the future.

Drawbacks or disadvantages of Deep Learning

• It requires very large amount of data in order to perform better than other techniques.
• It is extremely expensive to train due to complex data models. Moreover deep learning requires expensive GPUs and hundreds of machines. This increases cost to the users.
• There is no standard theory to guide you in selecting right deep learning tools as it requires knowledge of topology, training method and other parameters. As a result it is difficult to be adopted by less skilled people.
• It is not easy to comprehend output based on mere learning and requires classifiers to do so. Convolutional neural network based algorithms perform such tasks.

Source:

https://en.wikipedia.org/wiki/Deep_learning
https://pathmind.com/wiki/data-for-deep-learning
https://www.forbes.com/sites/bernardmarr/2018/10/01/what-is-deep-learning-ai-a-simple-guide-with-8-practical-examples/#495774dd8d4b
https://www.rfwireless-world.com/Terminology/Advantages-and-Disadvantages-of-Deep-Learning.html