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Course Outline
Machine Learning and Recursive Neural Networks (RNN) basics
- NN and RNN
- Backprogation
- Long short-term memory (LSTM)
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 101
- Prepare the Data
- Download
- 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
Advanced Usage
- Threading and Queues
- Distributed TensorFlow
- Writing Documentation and Sharing your Model
- Customizing Data Readers
- Using GPUs¹
- Manipulating TensorFlow Model Files
TensorFlow Serving
- Introduction
- Basic Serving Tutorial
- Advanced Serving Tutorial
- Serving Inception Model Tutorial
¹ The Advanced Usage topic, “Using GPUs”, is not available as a part of a remote course. This module can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64-bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.
Requirements
- Statistics
- Python
- (optional) A laptop with NVIDIA GPU that supports CUDA 8.0 and cuDNN 5.1, with 64-bit Linux installed
21 Hours