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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
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