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Course Outline
Machine Learning and Recurrent Neural Networks (RNN) basics
- NN and RNN
- Backpropagation
- Long Short-Term Memory (LSTM)
TensorFlow Basics
- Creating, initialising, saving, and restoring TensorFlow variables
- Feeding, reading, and preloading TensorFlow data
- How to use TensorFlow infrastructure to train models at scale
- Visualising and evaluating models with TensorBoard
TensorFlow Mechanics 101
- Tutorial files
- 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
- Customising data readers
- Using GPUs¹
- Manipulating TensorFlow model files
TensorFlow Serving
- Introduction
- Basic serving tutorial
- Advanced serving tutorial
- Serving Inception model tutorial
Convolutional Neural Networks
- Overview
- Goals
- Highlights of the tutorial
- Model architecture
- Code organisation
- CIFAR-10 model
- Model inputs
- Model prediction
- Model training
- Launching and training the model
- Evaluating a model
- Training a model using multiple GPU cards¹
- Placing variables and operations on devices
- Launching and training the model on multiple GPU cards
Deep learning for MNIST
- Setup
- Load MNIST data
- Start TensorFlow InteractiveSession
- Build a softmax regression model
- Placeholders
- Variables
- Predicted class and cost function
- Train the model
- Evaluate the model
- Build a multilayer convolutional network
- Weight initialisation
- Convolution and pooling
- First convolutional layer
- Second convolutional layer
- Densely connected layer
- Readout layer
- Train and evaluate the model
Image recognition
- Inception-v3
- C++
- Java
¹ Topics related to the use of GPUs are not available as part of a remote course. They 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
- Python
28 Hours
Testimonials (1)
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
