TensorFlow Training Course

Tensorflow Basics

•          Creation, Initializing, Saving and Restoring TensorFlow variables
•          Feeding, Reading and Preloading TensorFlow data
•          How to use TensorFlow infrastructure to train models at scale
•          Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics

•          Inputs and Placeholders
•          Build the Graph
  •    Inference
  •    Loss
  •    Training
•          Train the model
  •    The graph
  •    The session
  •    Train loop

•          Evaluate the model.
  •    Build the eval graph
  •    Eval output

The perceptron

•          Activation functions
•          The perceptron learning algorithm
•          Binary classification with the perceptron
•          Document classification with the perceptron
•          Limitations of the perceptron

Support Vector Machines

•          Kernels and the kernel trick.
•          Maximum margin classification and support vectors

Artificial Neural Networks

•          Nonlinear decision boundaries
•          Feedforward and feedback artificial neural networks
•          Multilayer perceptrons
•          Minimizing the cost function
•          Forward propagation
•          Back propagation
•          Improving the way neural networks learn

Convolutional Neural Networks

•          Goals
•          Model architecture
•          Principles
•          Code organization
•          Launching and training the model.
•          Evaluating a model.