- 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
- Inputs and Placeholders
- Build the GraphS
- Train the Model
- The Graph
- The Session
- Train Loop
- Evaluate the Model
- Build the Eval Graph
- Eval Output
- Activation functions
- The perceptron learning algorithm
- Binary classification with the perceptron
- Document classification with the perceptron
- Limitations of the perceptron
From the Perceptron to 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
- Model Architecture
- Code Organization
- Launching and Training the Model
- Evaluating a Model
Background in physics, mathematics and programming. Involvment in image processing activities.
I really appreciated the crystal clear answers of Chris to our questions.
I generally enjoyed the knowledgeable trainer.
I was amazed at the standard of this class - I would say that it was university standard.
Very good all round overview. Good background into why Tensorflow operates as it does.
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for.
I was benefit from topic selection. Style of training. Practice orientation.