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Course Outline
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 GraphS
- 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
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
- Goals
- Model Architecture
- Principles
- Code Organization
- Launching and Training the Model
- Evaluating a Model
Requirements
Background in physics, mathematics and programming. Involvment in image processing activities.
28 Hours
Testimonials (2)
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
Paul Lee
Course - TensorFlow for Image Recognition
Tomasz really know the information well and the course was well paced.