Course Outline
Foundations of Machine Learning and Recursive Neural Networks (RNN)
- Neural Networks (NN) and RNNs
- Backpropagation
- Long Short-Term Memory (LSTM)
TensorFundamentals
- Creating, initializing, saving, and restoring TensorFlow variables.
- Feeding, reading, and preloading data in TensorFlow.
- Leveraging TensorFlow infrastructure to train models at scale.
- Visualizing and evaluating models using TensorBoard.
TensorFlow Mechanics 101
- Data Preparation
- Downloading Data
- Inputs and Placeholders
- Graph Construction
- Inference
- Loss Functions
- Training Processes
- Model Training
- The Graph
- The Session
- Training Loop
- Model Evaluation
- Building the Evaluation Graph
- Evaluation Output
Advanced Usage
- Threading and Queues
- Distributed TensorFlow
- Documentation and Model Sharing
- Customizing Data Readers
- Utilizing GPUs¹
- Manipulating TensorFlow Model Files
TensorFlow Serving
- Introduction
- Basic Serving Tutorial
- Advanced Serving Tutorial
- Serving the Inception Model Tutorial
¹ The "Using GPUs" module under Advanced Usage is not available for remote courses. This module may be delivered in classroom-based sessions, subject to prior agreement, and only if both the trainer and all participants possess laptops with supported NVIDIA GPUs and 64-bit Linux installed (hardware not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.
Requirements
- Statistics
- Python
- (Optional) A laptop equipped with an NVIDIA GPU supporting CUDA 8.0 and cuDNN 5.1, running a 64-bit Linux operating system.
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.
