Challenges_Motivating_Deep_Learning.pptx

what are the challenges

1. Challenges Motivating Deep Learning A quick overview of why deep learning became necessary

2. 1. Manual Feature Engineering - Traditional ML needs hand-crafted features - DL learns features automatically from raw data

3. 2. Unstructured Data Handling - ML struggles with images, text, and audio - DL handles raw data using CNNs, RNNs, Transformers

4. 3. Learning Complex Patterns - Real-world problems require deep understanding - DL captures patterns layer-by-layer

5. 4. Big Data Explosion - Large-scale data from internet, sensors, etc. - DL models perform better with more data

6. 5. Advances in Computational Power - GPUs made deep model training faster and scalable - DL now feasible on large datasets

7. 6. End-to-End Learning - ML = feature extraction + model - DL = direct from input to output (single pipeline)

8. Traditional ML vs Deep Learning Traditional ML | Deep Learning Manual features | Learns features N eeds preprocessing | Raw data friendly Struggles on big data | Scales with big data Shallow models | Deep architectures S lower training | GPU-accelerated