Reference files/W9 - Deep Learning Learning Objectices.md

Architectural Understanding

1. Define deep learning as neural networks with multiple hidden layers that enable hierarchical feature learning (not just "3+ layers" but why depth matters for representation learning)
2. Explain why "deep" matters: each layer learns increasingly complex features
3. Compare how deep learning is different from machine learning, and be able to identify other deep learning methods

When to Use Deep Learning

4. Choose deep learning when you have: large datasets, unstructured data (images/text), complex patterns. Also understand why pretrained models work and when to fine-tune vs. feature extraction

5. Avoid deep learning when you need: explainable results, small datasets, or simple patterns

Understanding BERT

6. Explain what BERT does: understands word meaning based on context (unlike older methods)

7. Understand BERT training: pre-training with a massive dataset, masked language modeling, bidirectional learning and the transformer framework

8. Recognize why pretrained models save time and work better than training from scratch

Practical Implementation and Evaluation

10. Implement sentiment analysis using pretrained BERT via Hugging Face transformers

11. Evaluate model performance using appropriate metrics for classification tasks

12. Interpret the model's confidence scores and predictions

Notes

What is deep learning?
Video tutorial: Link

• Previously we learned what machine learning is
• Deep learning is a subset of machine learning
• A subfield of AI is ML -> Neural Network -> Deep Learning
  • More than three layers of neural network is considered deep neural network -> deep learning
  • Can ingest unstructured data and determine -> different from supervised learning -> unsupervised learning

When to use
Video: Link

• Unstructured data, like image, video, text
• High volumn of data -> deep learning will give you better result
• Complexity of feature -> complicated features -> deep learning
• Interpretability (important)
  • Industries like healthcare and finance require high interpretability, which is better answered by statistical ML
  • Deep learning’s complex neural networks makes it hard to interpret

BERT

• Google search is powered by BERT (bidirectional encoder representations from transformers)
  • BERT base, BERT large
• If you have two homes, how can you say if there are similar
  • For an object, if you can derive and compare features and compare their similarities…take all the numbers and create vectors and compare the vectors, you can then compare
• Generate feature vector for these words -> compare feature vector/word embedding
• How to generate word embeddings
• Word to vector (word2vec)
• Issues with word2vec -> you need a model that can generate contextualized meaning of words -> this is what BERT allows you to do

Pretrained BERT for sentiment analysis

• Download and install Transformer from huggingface
  • Install and import dependencies
  • Instantiat model - bert-base-multilingual-uncased-sentiment
• Perform sentiment scoring
  • Encode and calculate sentiment