Online or onsite, instructor-led live Deep Learning (DL) training courses demonstrate through hands-on practice the fundamentals and applications of Deep Learning and cover subjects such as deep machine learning, deep structured learning, and hierarchical learning.
Deep Learning 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 Deep Learning training can be carried out locally on customer premises in New Zealand or in NobleProg corporate training centers in New Zealand.
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
Very flexible
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
Topic. Very interesting!
Piotr
Course: Introduction to Deep Learning
Trainers theoretical knowledge and willingness to solve the problems with the participants after the training
Grzegorz Mianowski
Course: Introduction to Deep Learning
The topic is very interesting
Wojciech Baranowski
Course: Introduction to Deep Learning
Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.
Paul Kassis
Course: Advanced Deep Learning
The exercises are sufficiently practical and do not need a high knowledge in Python to be done.
Alexandre GIRARD
Course: Advanced Deep Learning
The global overview of deep learning
Bruno Charbonnier
Course: Advanced Deep Learning
The Colab Notebooks with the training and examples notes.
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
The exercises were very good and interactive. Instructors were always answering all questions and providing their insight on all topics
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
Course: Deep Learning for Telecom (with Python)
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
Coverage and depth of topics
Anirban Basu
Course: Machine Learning and Deep Learning
The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.
Jean-Paul van Tillo
Course: Machine Learning and Deep Learning
We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company
Sebastiaan Holman
Course: Machine Learning and Deep Learning
The trainers knowledge of the topics he was teaching.
Premier Partnership
Course: Python for Advanced Machine Learning
Having access to the notebooks to work through
Premier Partnership
Course: Python for Advanced Machine Learning
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Course: Python for Advanced Machine Learning
About face area.
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
Trainer was very knowledgeable and open to questions, liked to draw diagrams and explained things in a pretty good way
Course: Deep Learning with TensorFlow 2.0
I started with close to zero knowledge, and by the end I was able to build and train my own networks.
Huawei Technologies Duesseldorf GmbH
Course: TensorFlow for Image Recognition
Very updated approach or api (tensorflow, kera, tflearn) to do machine learning
Paul Lee
Course: TensorFlow for Image Recognition
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
Course: TensorFlow Extended (TFX)
Very knowledgeable
Usama Adam - TWPI
Course: Natural Language Processing with TensorFlow
The way he present everything with examples and training was so useful
Ibrahim Mohammedameen - TWPI
Course: Natural Language Processing with TensorFlow
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course: Natural Language Processing with TensorFlow
Trainer was very knowledgeable and open to questions, liked to draw diagrams and explained things in a pretty good way
Course: Deep Learning with TensorFlow 2.0
Deep Learning (DL) Subcategories in New Zealand
Deep Learning Course Outlines in New Zealand
Course Name
Duration
Overview
Course Name
Duration
Overview
21 hours
This instructor-led, live training in (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.
By the end of this training, participants will be able to:
- Understand advanced deep learning architectures and techniques for text-to-image generation.
- Implement complex models and optimizations for high-quality image synthesis.
- Optimize performance and scalability for large datasets and complex models.
- Tune hyperparameters for better model performance and generalization.
- Integrate Stable Diffusion with other deep learning frameworks and tools
21 hours
This instructor-led, live training in New Zealand (online or onsite) is aimed at beginner to intermediate-level data scientists and machine learning engineers who wish to improve the performance of their deep learning models.
By the end of this training, participants will be able to:
- Understand the principles of distributed deep learning.
- Install and configure DeepSpeed.
- Scale deep learning models on distributed hardware using DeepSpeed.
- Implement and experiment with DeepSpeed features for optimization and memory efficiency.
14 hours
This instructor-led, live training in New Zealand (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.
By the end of this training, participants will be able to:
- Set up a development environment that includes a popular LLM.
- Create a basic LLM and fine-tune it on a custom dataset.
- Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
- Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
7 hours
This instructor-led, live training in New Zealand (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.
By the end of this training, participants will be able to:
- Understand the basic principles of AlphaFold.
- Learn how AlphaFold works.
- Learn how to interpret AlphaFold predictions and results.
21 hours
This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.
By the end of this training, participants will be able to:
- Understand the principles of Stable Diffusion and how it works for image generation.
- Build and train Stable Diffusion models for image generation tasks.
- Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
- Optimize the performance and stability of Stable Diffusion models.
21 hours
In this instructor-led, live training in New Zealand, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.
By the end of this training, participants will be able to:
- Implement machine learning algorithms and techniques for solving complex problems.
- Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
- Push Python algorithms to their maximum potential.
- Use libraries and packages such as NumPy and Theano.
21 hours
This instructor-led, live training in New Zealand (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.
By the end of this training, participants will be able to:
- 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.
28 hours
In this instructor-led, live training in New Zealand, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning.
- Learn the applications and uses of deep learning in telecom.
- Use Python, Keras, and TensorFlow to create deep learning models for telecom.
- Build their own deep learning customer churn prediction model using Python.
14 hours
Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.
This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.
By the end of this training, participants will be able to:
- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
21 hours
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
21 hours
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
21 hours
Caffe is a deep learning framework made with expression, speed, and modularity in mind.
This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example
Audience
This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.
After completing this course, delegates will be able to:
- understand Caffe’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, implementing layers and logging
21 hours
This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.
28 hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.
21 hours
Introduction:
Deep learning is becoming a principal component of future product design that wants to incorporate artificial intelligence at the heart of their models. Within the next 5 to 10 years, deep learning development tools, libraries, and languages will become standard components of every software development toolkit. So far Google, Sales Force, Facebook, Amazon have been successfully using deep learning AI to boost their business. Applications ranged from automatic machine translation, image analytics, video analytics, motion analytics, generating targeted advertisement and many more.
This coursework is aimed for those organizations who want to incorporate Deep Learning as very important part of their product or service strategy. Below is the outline of the deep learning course which we can customize for different levels of employees/stakeholders in an organization.
Target Audience:
( Depending on target audience, course materials will be customized)
Executives
A general overview of AI and how it fits into corporate strategy, with breakout sessions on strategic planning, technology roadmaps, and resource allocation to ensure maximum value.
Project Managers
How to plan out an AI project, including data gathering and evaluation, data cleanup and verification, development of a proof-of-concept model, integration into business processes, and delivery across the organization.
Developers
In-depth technical trainings, with focus on neural networks and deep learning, image and video analytics (CNNs), sound and text analytics (NLP), and bringing AI into existing applications.
Salespersons
A general overview of AI and how it can satisfy customer needs, value propositions for various products and services, and how to allay fears and promote the benefits of AI.
14 hours
This instructor-led, live training in New Zealand (online or onsite) is aimed at business analysts, data scientists, and developers who wish to build and implement deep learning models to accelerate revenue growth and solve problems in the business world.
By the end of this training, participants will be able to:
- Understand the core concepts of machine learning and deep learning.
- Get insights on the future of business and industry with ML and DL.
- Define business strategies and solutions with deep learning.
- Learn how to apply data science and deep learning in solving business problems.
- Build deep learning models using Python, Pandas, TensorFlow, CNTK, Torch, Keras, etc.
28 hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in finance
- Use R to create deep learning models for finance
- Build their own deep learning stock price prediction model using R
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.
In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use Python, Keras, and TensorFlow to create deep learning models for banking
- Build their own deep learning credit risk model using Python
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use R to create deep learning models for banking
- Build their own deep learning credit risk model using R
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.
In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in finance
- Use Python, Keras, and TensorFlow to create deep learning models for finance
- Build their own deep learning stock price prediction model using Python
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep Learning is a subfield of Machine Learning which attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine.
In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations.
By the end of this training, participants will be able to:
- Understand the fundamentals of Deep Learning
- Learn Deep Learning techniques and their applications in the industry
- Examine issues in medicine which can be solved by Deep Learning technologies
- Explore Deep Learning case studies in medicine
- Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine
- Managers
- Medical professionals in leadership roles
- Part lecture, part discussion, exercises and heavy hands-on practice
- To request a customized training for this course, please contact us to arrange.
14 hours
This instructor-led, live training in New Zealand (online or onsite) is aimed at developers and data scientists who wish to apply convolutional neural networks (CNNs) to the analysis of MRI scans.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Analyze MRI images using deep learning techniques such as CNNs.
- Detect potential health conditions such as heart disease through MRI scan analysis.
- Apply techniques such as image segmentation and CNN training to identify potential disease.
- Identify the genomics of a disease using radiomics.
- Build and deploy a deep learning application aimed at healthcare image analysis.
21 hours
Audience
This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images
This course provide working examples.
7 hours
In this instructor-led, live training, participants will learn how to use DSSTNE to build a recommendation application.
By the end of this training, participants will be able to:
- Train a recommendation model with sparse datasets as input
- Scale training and prediction models over multiple GPUs
- Spread out computation and storage in a model-parallel fashion
- Generate Amazon-like personalized product recommendations
- Deploy a production-ready application that can scale at heavy workloads
- Part lecture, part discussion, exercises and heavy hands-on practice
7 hours
In this instructor-led, live training, participants will learn how to use Facebook NMT (Fairseq) to carry out translation of sample content.
By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.
- Part lecture, part discussion, heavy hands-on practice
- If you wish to use specific source and target language content, please contact us to arrange.
21 hours
Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject.
14 hours
In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.
By the end of this training, participants will be able to:
- 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
21 hours
Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks.
In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images.
By the end of this training, participants will be able to:
- 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.
21 hours
This course covers AI (emphasizing Machine Learning and Deep Learning)
Last Updated:
Other regions in New Zealand
Other countries
Consulting in New Zealand
Online Deep Learning courses, Weekend DL (Deep Learning) courses, Evening Deep Learning training, Deep Learning boot camp, Deep Learning (DL) instructor-led, Weekend Deep Learning training, Evening DL (Deep Learning) courses, Deep Learning coaching, Deep Learning instructor, Deep Learning trainer, Deep Learning (DL) training courses, Deep Learning (DL) classes, Deep Learning (DL) on-site, DL (Deep Learning) private courses, Deep Learning (DL) one on one training
