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
Machine Learning and Recursive Neural Networks (RNN) basics
- NN and RNN
- Backpropagation
- 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
- 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
- Customizing 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 Organization
- 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 Initialization
- 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 a 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
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €9120 online delivery, based on a group of 2 delegates, €2880 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
Contact us for an exact quote and to hear our latest promotions
Public Training
Please see our public courses
Testimonials (1)
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
