Deep Learning vs Machine Learning | IABAC
- 1. Deep Learning vs. Machine Learning This presentation provides a comparative overview of deep learning and machine learning. We'll explore their key differences, data requirements, and applications. Join us as we delve into the future of AI! by Vamsi Kumar
- 2. What is Machine Learning? Definition ML enables systems to learn from data. Systems make decisions without explicit programming. Key Components Algorithms Data Models Types of ML Supervised learning Unsupervised learning Reinforcement learning
- 3. What is Deep Learning? 1 Definition DL uses deep neural networks. It learns from vast datasets, a subset of ML. 2 Key Components Neural networks with input, hidden, and output layers are key. 3 Applications Used in image, speech recognition, and NLP. For example, self driving cars.
- 4. Key Differences: Conceptual ML Relies on feature engineering. Models are often simpler. DL Learns features automatically. Neural networks are multi-layered. Power ML requires less computing power. DL needs substantial power.
- 5. Data Requirements 1 Machine Learning Works well with smaller datasets. Requires hand- crafted features to function. 2 Deep Learning Needs large datasets to perform well. Can automatically extract features.
- 6. Model Interpretability 1 Machine Learning Easier to interpret insights into data patterns and relationships. 2 Deep Learning Often a "black box," decisions are hard to determine and trace back.
- 7. Performance and Accuracy 1 2 3 4 ML Effectiveness Works well with small datasets. ML Performance Good with feature engineering. DL Superior On large, complex datasets. DL Accurate Image and speech recognition.
- 8. Computational Resources 1 Machine Learning Requires few resources. 2 CPUs Run on less powerful machines. 3 Deep Learning Needs GPUs, TPUs.
- 9. Use Cases Machine Learning Fraud detection is very common. Deep Learning DL powers self-driving cars. Deep Learning Used in natural language processing.
- 10. Conclusion ML Simple Easy to use, interpret, and implement. DL Powerful Good for very complex datasets. AI Future Both advance artificial intelligence.