Deep learning notes Link to heading
Deep Learning is a subset of Machine Learning and this one is a subset of Artificial Intelligence.
Required Math:
- Linear Algebra
- Calculus
- Probability and statistics
Frameworks: Numpy, Pnadas, Matplotlib, tensorflow, Keras, pytorch, hugging Face
Keys:
- Data Preprocessing: data cleaning, normalization
- ML algos: linear regression, decision trees, random forest, SVM, K-NN
- Evaluaction metrics: Accuracy, precision, Recall, F1 Score
- Deeplearning: neural networks. CNNs, RNNs, GANs, Transformers
- NPL: text mining, sentiment analysis
- Reinforcement learning: q-learning, policy gradients

LLMs: GPT (openai), BERT (google), Roberta (facebook)
Models:
- Bert for undesratndinc a context in a text
- GPTs (2, 3, etc) generates texts
- T5 for translations and summarization