Deep Learning vs. Conventional Machine Learning
summary
Compares concrete models and results: a triplet Siamese encoder with pretrained fastText embeddings LSTM attention and triplet loss on 20k QA pairs outperforms XGBoost with hand‑crafted NLP features and still beats it when trained on 10% of data; an LSTM/attention model on 50 million Amazon reviews yields higher accuracy than Naive Bayes and random forest for five‑way sentiment; transfer learning from ResNet34 on 6k cervical images far exceeds SVM visual‑bag‑of‑words.
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chapters (19)
- 0:00Deep vs conventional ML
- 1:04From traditional to deep networks
- 3:30Why deep networks need new techniques
- 6:00Advantages of deep learning
- 8:22Automated feature learning
- 10:31Techniques from traditional ML
- 12:38Dropout as innovation
- 14:22ReLU activation functions
- 16:52Batch normalization roots
- 18:22Question answering case study
- 23:22Sentiment analysis case study
- 26:56Healthcare image classification case study
- 30:54Transfer learning for small datasets
- 33:00Challenges in deep learning
- 35:11Computational limitations
- 35:55Data labeling bottlenecks
- 36:58Deep learning in tabular data
- 37:20Tooling and frameworks
- 38:02Conclusion and outlook
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