Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Fundamentals of Machine Learning and Recurrent Neural Networks (RNN)
- Neural Networks (NN) and RNNs
- Backpropagation
- Long Short-Term Memory (LSTM)
TensorFlow Fundamentals
- Creating, initialising, saving, and restoring TensorFlow variables
- Feeding, reading, and preloading TensorFlow data
- Leveraging TensorFlow infrastructure to train models at scale
- Visualising and evaluating models using TensorBoard
TensorFlow Mechanics 101
- Tutorial files
- Preparing the data
- Download
- Inputs and placeholders
- Building the graph
- Inference
- Loss
- Training
- Training the model
- The graph
- The session
- Training loop
- Evaluating the model
- Constructing the evaluation graph
- Evaluation 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 the Inception model tutorial
Convolutional Neural Networks
- Overview
- Goals
- Tutorial highlights
- 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
- Loading MNIST data
- Starting the TensorFlow InteractiveSession
- Building a softmax regression model
- Placeholders
- Variables
- Predicted class and cost function
- Training the model
- Evaluating the model
- Building a multilayer convolutional network
- Weight initialisation
- Convolution and pooling
- First convolutional layer
- Second convolutional layer
- Densely connected layer
- Readout layer
- Training and evaluating 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 equipped 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.
