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Introduction to Neural Networks with Python
- 2. dataHacker.rs NN Intro Logistic Regression Forward Propagation Cost Function Backward Propagation Neural Network Brain Analogy Logistic Regression Implementation
- 3. dataHacker.rs Logistic Regression Supervised learning algorithm Binary classification problem Labels: 0 or 1 Example: cat picture
- 4. dataHacker.rs Logistic Regression Linear function Parameters: w, b Sigmoid Activation function Range 0-1 “weights” “bias vector” “input data” “output”
- 5. dataHacker.rs Simple Neuron Logistic regression One neuron – two functions 1 -Linear function 2 - Activation function
- 6. dataHacker.rs Computation Graph Forward propagation & Backward propagation Cost Function Measures how well our algorithm is working
- 7. dataHacker.rs Key components Three key components to train Neural Networks 1. Score function (Linear+Activation) 2. Loss Function 3. Optimization
- 8. dataHacker.rs Forward propagation “input data” “weight” “bias” Linear function Activation function Loss function Logistic Regression
- 9. dataHacker.rs Cost Function How our algorithm works? Visualizing the cost function
- 10. dataHacker.rs Backward propagation Optimization Gradient descent Learning rate What is the correct value of parameters?
- 11. dataHacker.rs Backward propagation Gradient of L with respect to parameters Calculate derivatives Local gradient X Upstream gradient
- 12. dataHacker.rs Neural Network Input layer Hidden layers Output layer
- 13. dataHacker.rs Neural Network Logistic Regression Neural Network with 5 hidden layers “shallow” “shallow” “shallow” “deep” Neural Network with single hidden layer Neural Network with 2 hidden layers