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Classroom Contents
Introduction to Neural Networks in Python - Tensorflow-Keras
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- 1 Video overview
- 2 Why use neural networks
- 3 How neural nets work architecture basics
- 4 Hyperparameter overview batch size, optimizer, dropout, learning rate, epochs
- 5 How do we choose layers, neurons, & other parameters?
- 6 Why do we need an activation function?
- 7 What activation function should I use?
- 8 Keras vs Tensorflow vs PyTorch
- 9 Coding starts github & setup
- 10 Writing our first neural network linear example
- 11 Selecting optimizer & loss function model.compile
- 12 Fitting training data to our model model.fit
- 13 Shuffle order of training data
- 14 Evaluate model on test data model.evaluate
- 15 Example #2: Classifying quadratic data
- 16 Example #3: Classifying 6 clusters of data try on your own
- 17 Using network to predict a single data point model.predict
- 18 Example #4: Classifying multiple labels at a time BinaryCrossentropy loss
- 19 Example #5: Classifying our complex data from start of video
- 20 Conclusion & Next steps of learning neural nets
