
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 the Philippines or in NobleProg corporate training centers in the Philippines.
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
The structure from first principles, to case studies, to application.
Margaret Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Course: Introduction to Deep Learning
The deep knowledge of the trainer about the topic.
Sebastian Görg
Course: Introduction to Deep Learning
I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient
Radek
Course: Introduction to Deep Learning
Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.
Dolby Poland Sp. z o.o.
Course: Introduction to Deep Learning
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
Very flexible
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
Practical exercises
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Course: Advanced Deep Learning
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course: Advanced 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
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 depth of knowledge & explanations, he could explain difficult concepts quite intuitively!
KnowledgePool
Course: Python for Advanced Machine Learning
The trainer was very knowledgeable, he was able to answer every question, was able to bug fix coding issues, and could tie a lot of the topics into his real life experiences. The trainer's knowledge applied to a different approach to coding (see above) would have been perfect.
Premier Partnership
Course: Python for Advanced Machine Learning
Seeing the practical examples
Premier Partnership
Course: Python for Advanced Machine Learning
Trainer knowledge and experience on subject matter is very deep
Premier Partnership
Course: Python for Advanced Machine 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
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
The visualisations were popular. I think they inspired some attendees to have more interest in the subject. It was also clear that the trainer knew a lot about the subject.
ARM Ltd.
Course: Neural computing – Data science
code examples:-)
Marcin - Marta Skiba, P4 Sp. z o.o.
Course: Deep Learning for Telecom (with Python)
I really liked the demos and the content.
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
I liked that the instructor had many pre-written scripts to show off many different aspects of ML and AI. I really enjoyed being able to see live demos of so many ways ML and AI is being used. Much of what we covered was cutting edge technology that is still in its early stages of development.
Matthew Pepper - Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
The last two days went more into state of the art and available tools that exist for training and deploying models. Also getting a better understanding of pytorch was very useful for me as someone who was only familiar with keras but have been seeing more and more implementations in pytorch.
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
The instructors were super knoweledgeable and skilled at conjuring up anything we could ask for examples on. That was amazing. Hopefully we can get to that level in time.
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
The colab notebooks we get to keep
Palmer Greer - Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
Breadth of content was good, even though the main focus seemed more on image/video processing.
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
The clarity with which it was presented
John McLemore - Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
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)
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
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
Topic selection. Style of training. Practice orientation
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane
Course: Neural Networks Fundamentals using TensorFlow as Example
Very good all round overview.Good background into why Tensorflow operates as it does.
Kieran Conboy
Course: Neural Networks Fundamentals using TensorFlow as Example
I was amazed at the standard of this class - I would say that it was university standard.
David Relihan
Course: Neural Networks Fundamentals using TensorFlow as Example
Knowledgeable trainer
Sridhar Voorakkara
Course: Neural Networks Fundamentals using TensorFlow as Example
Deep Learning Subcategories in the Philippines
DL (Deep Learning) Course Outlines in the Philippines
- 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.
- 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.
- Understand the basic principles of AlphaFold.
- Learn how AlphaFold works.
- Learn how to interpret AlphaFold predictions and results.
- 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.
- 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.
- 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.
- 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.
- 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
- 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
- Lecture and discussion coupled with hands-on exercises.
- Part lecture, part discussion, heavy hands-on practice
- Part lecture, part discussion, heavy hands-on practice
- If you wish to use specific source and target language content, please contact us to arrange.
- 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
- 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
- Install tensor2tensor, select a data set, and train and evaluate an AI model
- Customize a development environment using the tools and components included in Tensor2Tensor
- Create and use a single model to concurrently learn a number of tasks from multiple domains
- Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited
- Obtain satisfactory processing results using a single GPU
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
- Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
- Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
- Understand and implement unsupervised learning techniques
- Apply clustering and classification to make predictions based on real world data.
- Visualize data to quicly gain insights, make decisions and further refine analysis.
- Improve the performance of a machine learning model using hyper-parameter tuning.
- Put a model into production for use in a larger application.
- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
- 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
- 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
- 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
- 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
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