JNTUK DEEP LEARNING Important Questions | B.Tech | R16,R19,R20

JNTUK DEEP LEARNING Important Questions | DP Important Questions | JNTUK DP Imp Questions | B.TECH | R16,R19,R20 | Deep Learning imp questions 2024

Introduction (dp): Deep Learning (dp) models are composed of multiple layers of interconnected nodes (neurons) that process data in a hierarchical fashion. Each layer extracts features from the input data, and these features are then passed to the next layer for further processing.

Features of Deep Learning (dp):

1. Automatic feature extraction
2. Ability to handle complex, high-dimensional data
3. End-to-end learning
4. Scalability with increasing data and computational power

Advantages of Deep Learning (dp):

1. High accuracy in many domains, including computer vision, natural language processing, and speech recognition.
2. Ability to learn from raw data without manual feature engineering.
3. Transfer learning capabilities, allowing models to be reused for related tasks.

Disadvantages of Deep Learning (dp):

1. Requires large amounts of labeled data for training.
2. Can be computationally intensive, requiring powerful hardware.
3. Models are often opaque and difficult to interpret (black box).
4. Susceptible to adversarial attacks and data biases.

Applications of Deep Learning (dp):

1. Computer Vision: Image recognition, object detection, and segmentation.
2. Natural Language Processing (NLP): Machine translation, text generation, and sentiment analysis.
3. Speech Recognition and Synthesis
4. Recommendation Systems
5. Autonomous Vehicles
6. Healthcare: Medical image analysis and drug discovery.

In summary, Deep Learning (dp) is a powerful machine learning technique that has revolutionized many fields by enabling accurate and scalable models for complex tasks. However, it also has limitations and challenges that require careful consideration and responsible development.

Deep Learning Important Questions