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| **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](https://www.youtube.com/watch?v=q6kJ71tEYqM) | |
| - 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](https://www.youtube.com/watch?v=o3bWqPdWJ88) | |
| - 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 | |