Introduction to TensorFlow Contents



Introduction to different types of AI models, and the focus of the class.

Tensorflow Linear Regression

This section discusses Placeholders, Variables, Sessions, Running sessions, optimization, and calculating error

Exercise 1

Experiment with a predefined TensorFlow model to change learning rate, epochs and more

Tensorflow Classification

This section discusses Softmax definition, Cross Entropy definition, error propagation, Tensorflow Optimizers, Learning rate, and epochs

Exercise 2

Experiment with a predefined TensorFlow MNIST classifier

Tensorflow Deep Networks

This section discusses Tensorflow activation functions, Tensorflow differentiation, Tensorflow error propagation, Tensorflow Deep Network optimization, and Deep Network descriptions

Exercise 3

Experiment with a predefined TensorFlow Deep MNIST classifier including adding extra layers

Visualizing Model Operation

This section discusses Tensorboard, adding summaries, adding histograms, adding graphs, interpreting results

Exercise 4

Use Tensorboard to visualize the internal workings of a TensorFlow model during training and evaluation

Convolutional Neural Networks

This section discusses Convolutional filters, feature maps, convolutional layers, pooling layers, fully connected layers, stride, padding, constructing CNN networks, training CNN networks

Exercise 5

Experiment with a predefined TensorFlow CNN model. Change hyper parameters and see what happens

TensorFlow Estimators

This section discusses TensorFlow Built-In Estimators, Estimator behavior, Why use estimators?

Exercise 6

Experiment with a predefined TensorFlow Estimator model

Transfer Learning

What makes a good data set, balanced data sets, distinct data sets, non-conflicting data, ImageNet, Inception-V3, transfer learning description, transfer learning operation, transfer learning

Exercise 7

Experiment with using TensorFlow scripts to retrain a TensorFlow Inception-V3 network using different training data