# Final review

Logistic regression

- sigmoid curve
- loss function, how it learns (weight updates)
- “gradient descent”
- be comfortable with a vector of X values (not just single value)
- bias term

Tensorflow

- logistic regression in Tensorflow
- “placeholder”, “variable”, epochs, loss, optimizer (“SGD”)

Pandas

- “dummies”, “one-hot”

Non-binary outputs

- using softmax
- what the loss function changes to
- (MNIST code - 10 outputs)

Multi-layer networks

- how this is coded in Tensorflow (not Keras)
- how this is coded in Keras
- how many weights/biases there are (parameters)
- epochs, batch size, train/test splits, validation data
- overfitting, underfitting

Text processing

- tokenization, bag of words
- stop words
- word2vec, “embeddings”

Deep learning

- convolutions
- how it works: sliding kernel
- padding, stride, kernel size
- if you see a statement like: Convolution2D(32, kernel=(3,3), …) what does it mean

- dropout
- pooling
- transfer learning
- image loading and transformations (random zooming, rotation, etc.)