Deep Learning

The field of AI is something where machines can perform tasks that normally requires human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without any human involvement.

Deep learning is the subfield of machine learning, supporting algorithms that are inspired by the structure and function of the human brain, and named as artificial neural networks.

Deep learning is the one category of machine learning that emphasizes training the computer about the basic instincts of human beings. In deep learning, a computer algorithm learns to perform classification tasks directly on complex data in the form of images, text, or sound.

These algorithms can accomplish state-of-the-art (SOTA) accuracy, and even sometimes surpassing human-level performance. They are trained with the large set of labeled data and neural network architectures, involving many layers.

Moreover:

• Deep Learning is a prime technology behind the technology such as virtual assistants, facial recognition, driverless cars, etc.
• The working of deep learning involves training the data and learning from the experiences.
• With the increasing depth of the data, this training performance and deep learning capabilities have been improved drastically, and this is because it is broadly adopted by data experts.

Benefits:

• Along with the ample amount of benefits, threats also surfaces due to the unexplored capabilities of deep learning.Along with the ample amount of benefits, threats also surfaces due to the unexplored capabilities of deep learning.
• It imitates the functionality of a human brain for managing the data and forming the patterns for referring it in decision making.
• The trained dataset can be interconnected, diverse and complex in nature.
• The larger the data set, the more efficient the training that directly impacts the decision making.

Curriculum:

• Deep Learning And Human Brain
• Artificial Neural Network
• Step 1 - Calculate Weighted Sum
• Step 2 - Activation Function
• Shallow Neural Network
• Deep Neural Networks
• Applications
• Challenges In Deep Learning