Topic outline
Table of contents
- Lecture #1: Introduction to Neural Networks | Deep Learning and Neural Networks
- Lecture #2: Supervised Learning | Deep Learning and Neural Networks
- Lecture #3: Why is Deep Learning getting Popular? | Deep Learning and Neural Networks
- Lecture #4: Binary Classification | Deep Learning and Neural Networks
- Lecture #5: Logistic Regression | Deep Learning and Neural Networks
- Lecture #6: Logistic Regression Cost Function | Deep Learning and Neural Networks
- Lecture #7: Gradient Descent | Deep Learning and Neural Networks
- Lecture #8: Derivatives [Part 1] | Derivative from First Principle
- Lecture #9: Derivatives [Part 2] | Derivative of x2 sinx cosx ln x polynomials | Power rule
- Lecture #10: Derivatives [Part 3] | Sum Rule
- Lecture #11: Derivatives [Part 4] | Derivative of e^x (Exponential Function)
- Lecture #12: Derivatives [Part 5] | Product Rule of Differentiation
- Lecture #13: Derivatives [Part 6] | Quotient Rule of Differentiation
- Lecture #14: Computation Graph | Deep Learning
- Lecture #15: Derivatives with Computation Graph | Backpropagation | Deep Learning
- Lecture #16: Logistic Regression Gradient Descent | Deep Learning
- Lecture #17: Gradient Descent on m Examples | Deep Learning
- Lecture #18: Vectorization | Deep Learning
- Lecture #19: Vectorization examples using NumPy | Deep Learning
- Lecture #20: Vectorizing Logistic Regression Forward Propagation | Deep Learning
- Lecture #21: Vectorizing Logistic Regression Backpropagation | Deep Learning
- Lecture #22: NumPy - Overview | Deep Learning
- Lecture #23: NumPy - Creating Arrays | Deep Learning
- Lecture #24: NumPy - linspace, zeros, ones | Deep Learning
- Lecture #25: NumPy - Indexing Arrays | Deep Learning
- Lecture #26: NumPy - Slicing NumPy Arrays | Deep Learning
- Lecture #27: NumPy - Broadcasting NumPy Arrays | Deep Learning
- Lecture #28: One hidden layer Neural Network | Deep Learning
- Lecture #29: Neural Network Representation | Deep Learning
- Lecture #30: Neural Network Computation | Deep Learning
- Lecture #32: Activation Functions Neural Networks | Deep Learning