Introduction to Deep Learning

Deep Learning: Subfield of machine learning that focuses on training artificial Neural Network models to learn and make prediction or decesion directly from raw data.

Artificial Neural Network: Inspired by the structured and functioning of the human brain, specially the interconnected network of neuron.

Deep Artifical Neural Network: Consist of multi layers of perception or neurons.

NOTE: When here about the deep learning just think about a large, deep neural networks. Deep refers to large number of layers in depth typically in the Neural network.

Deep learning feature:

• Build feature automitacall based on training data.
• Combine feature extraction and recognation task.

"Deep Learning extract features automically from raw data, allowing Neural Network to learn hierarical representation of data themselves."

Feature extractioin:

•  Feature representation.
• Learns parameter to perform to task such as classification, object detection, segmentation.

Real world application:

1. Computer vision
2. Natural Language Processing (NLP)
3. Fruad detection
4. Medical and healthcare Diagnosis
5. Speech Recognition
6. Recommendation System
7. Robotics and automation
8. Smart surveillance system
9. Autonomous vehicle vision systems
10. Agriculture and smart farming