Course Outline
Machine Learning and Recursive Neural Networks (RNN) fundamentals
- NN and RNN
- Backpropagation
- Long short-term memory (LSTM)
TensorFlow Basics
- Creating, initializing, saving, and restoring TensorFlow variables
- Feeding, reading, and preloading TensorFlow data
- Leveraging TensorFlow infrastructure to train models at scale
- Visualizing and evaluating models using TensorBoard
TensorFlow Mechanics 101
- Prepare the Data
- Download
- Inputs and Placeholders
- Construct 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
- Documentation and Sharing your Model
- Customizing Data Readers
- Utilizing GPUs¹
- Manipulating TensorFlow Model Files
TensorFlow Serving
- Introduction
- Basic Serving Tutorial
- Advanced Serving Tutorial
- Serving Inception Model Tutorial
¹ The "Using GPUs" segment within Advanced Usage is not included in remote courses. This module may be delivered during classroom-based sessions, subject to prior agreement, and only if both the trainer and all participants possess laptops with compatible NVIDIA GPUs and 64-bit Linux installed (hardware is not provided by NobleProg). NobleProg cannot guarantee that trainers will have the requisite hardware available.
Requirements
- Statistics
- Python
- (optional) A laptop equipped with an NVIDIA GPU that supports CUDA 8.0 and cuDNN 5.1, running 64-bit Linux
Testimonials (2)
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
Magdalena - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
Tomasz really know the information well and the course was well paced.
