Neural Networks-Neural networks intuition
Welcome!
()
Neurons and the brain
()
Demand Prediction
()
Example: Recognizing Images
()
Neural Networks-Neural network model
Neural network layer
()
More complex neural networks
()
Inference: making predictions (forward propagation)
()
Neural Networks-TensorFlow implementation
Inference in Code
()
Data in TensorFlow
()
Building a neural network
()
Neural Networks-Neural network implementation in Python
Forward prop in a single layer
()
General implementation of forward propagation
()
Neural Networks-Speculations on artificial general intelligence (AGI)
Is there a path to AGI?
()
Neural Networks-Vectorization(optional)
How neural networks are implemented efficiently
()
Matrix multiplication
()
Matrix multiplication rules
()
Matrix multiplication code
()
Neural network training-Neural Network Training
TensorFlow implementation
()
Training Details
()
Neural network training-Activation Functions
Alternatives to the sigmoid activation
()
Choosing activation functions
()
Why do we need activation functions?
()
Neural network training-Multiclass Classification
Multiclass
()
Softmax
()
Neural Network with Softmax output
()
Improved implementation of softmax
()
Classification with multiple outputs (Optional)
()
Neural network training-Additional Neural Network Concepts
Advanced Optimization
()
Additional Layer Types
()
Neural network training-Back Propagation (Optional)
What is a derivative? (Optional)
()
Computation graph (Optional)
()
Larger neural network example (Optional)
()
Advice for applying machine learning-Advice for applying machine learning
Deciding what to try next
()
Evaluating a model
()
Model selection and training/cross validation/test sets
()
Advice for applying machine learning-Bias and variance
Diagnosing bias and variance
()
Regularization and bias/variance
()
Establishing a baseline level of performance
()
Learning curves
()
Deciding what to try next revisited
()
Bias/variance and neural networks
()
Advice for applying machine learning-Machine learning development process
Iterative loop of ML development
()
Error analysis
()
Adding data
()
Transfer learning: using data from a different task
()
Full cycle of a machine learning project
()
Fairness, bias, and ethics
()
Advice for applying machine learning-Skewed datasets (optional)
Error metrics for skewed datasets
()
Trading off precision and recall
()
Decision trees-Decision trees
Decision tree model
()
Learning Process
()
Decision trees-Decision tree learning
Measuring purity
()
Choosing a split: Information Gain
()
Putting it together
()
Using one-hot encoding of categorical features
()
Continuous valued features
()
Regression Trees (optional)
()
Decision trees-Tree ensembles
Using multiple decision trees
()
Sampling with replacement
()
Random forest algorithm
()
XGBoost
()
When to use decision trees
()
Decision trees-End of Access to Lab Notebooks
[IMPORTANT] Reminder about end of access to Lab Notebooks
Decision trees-Conversations with Andrew (Optional)
Andrew Ng and Chris Manning on Natural Language Processing
()
Decision trees-Acknowledgments
Acknowledgements