
Online or onsite, instructor-led live Neural Network training courses demonstrate through interactive discussion and hands-on practice how to construct Neural Networks using a number of mostly open-source toolkits and libraries as well as how to utilize the power of advanced hardware (GPUs) and optimization techniques involving distributed computing and big data. Our Neural Network courses are based on popular programming languages such as Python, Java, R language, and powerful libraries, including TensorFlow, Torch, Caffe, Theano and more. Our Neural Network courses cover both theory and implementation using a number of neural network implementations such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).
Neural Network training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Neural Networks trainings in New Zealand can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Testimonials
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Working with real industry-leading ML tools, real datasets and being able to consult with a very experienced data scientist.
Zakład Usługowy Hakoman Andrzej Cybulski
Course: Applied AI from Scratch in Python
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Course: Applied AI from Scratch in Python
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Course: Applied AI from Scratch in Python
Very flexible
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
Abhi always made sure we were following along. Good mix of practice and theory.
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Course: Deep Reinforcement Learning with Python
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
Explore
Course: Deep Reinforcement Learning with Python
Really simple, easy to follow explanations Covered everything necessary in enough detail to understand fully, but so that it was not overwhelming good mix of theory and practice
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Course: Introduction to the use of neural networks
the interactive part, tailored to our specific needs
Thomas Stocker
Course: Introduction to the use of neural networks
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
Course: Introduction to the use of neural networks
Graphs in R :)))
Faculty of Economics and Business Zagreb
Course: Neural Network in R
We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.
Tea Poklepovic
Course: Neural Network in R
new insights in deep machine learning
Josip Arneric
Course: Neural Network in R
Neural Networks Subcategories in New Zealand
Neural Networks Course Outlines in New Zealand
- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
- Apply advanced Reinforcement Learning algorithms to solve real-world problems.
- Build a Deep Learning Agent.
- Set up the necessary development environment to start developing neural network models.
- Define and implement neural network models using a comprehensible source code.
- Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
- Implement different neural networks optimization techniques to resolve underfitting and overfitting
- Understand and choose from a number of neural network architectures
- Implement supervised feed forward and feedback networks
- Developers
- Analysts
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
- Prepare data for neural networks using the normalization process
- Implement feed forward networks and propagation training methodologies
- Implement classification and regression tasks
- Model and train neural networks using Encog's GUI based workbench
- Integrate neural network support into real-world applications
- Developers
- Analysts
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
- Build a deep learning model
- Automate data labeling
- Work with models from Caffe and TensorFlow-Keras
- Train data using multiple GPUs, the cloud, or clusters
- Developers
- Engineers
- Domain experts
- Part lecture, part discussion, exercises and heavy hands-on practice
- Gain an overview of artificial intelligence, machine learning, and computational intelligence.
- Understand the concepts of neural networks and different learning methods.
- Choose artificial intelligence approaches effectively for real-life problems.
- Implement AI applications in mechatronic engineering.
- Access CNTK as a library from within a Python, C#, or C++ program
- Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)
- Use the CNTK model evaluation functionality from a Java program
- Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)
- Scale computation capacity on CPUs, GPUs and multiple machines
- Access massive datasets using existing programming languages and algorithms
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
- If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.
- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
- Create recommender systems at scale.
- Apply collaborative filtering to build recommender systems.
- Use Apache Spark to compute recommender systems on clusters.
- Build a framework to test recommendation algorithms with Python.
- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
- Train various types of neural networks on large amounts of data.
- Use TPUs to speed up the inference process by up to two orders of magnitude.
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
- have a good understanding on deep neural networks(DNN), CNN and RNN
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, building graphs and logging
- Lecture and discussion coupled with hands-on exercises.
Last Updated: