Available online or onsite, our instructor-led live Deep Learning (DL) training programs provide a hands-on approach to mastering the core principles and practical applications of Deep Learning. These courses explore key areas such as deep machine learning, deep structured learning, and hierarchical learning.
Deep Learning training is offered in two formats: "online live training" (also known as "remote live training") and "onsite live training". Online sessions are conducted through an interactive remote desktop, while onsite training can be delivered locally at your customer premises in Lille or at NobleProg's corporate training centers in Lille.
NobleProg -- Your Local Training Provider
Lille, Gare Flandres
NobleProg Lille, 21 Avenue le Corbusier, Lille, france, 59800
In front of Flandres TGV Train Station
From Lille train stations
From Lille-Flandres: the address is essentially right next to the station — about 1–3 minutes on foot.
From Lille-Europe: walk toward Lille-Flandres along Avenue Le Corbusier. It is roughly 5–10 minutes on foot.
So you don't need a bus or metro if you're arriving by train.
If you're coming by bus
The nearest bus stops to Avenue Le Corbusier include Lion d'Or and Jacquet. Several local lines serve the area, including 13, 86, L5 and L91.
This instructor-led, live training in Lille (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.
By the end of this training, participants will be able to:
Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
Develop and optimize AI models using TensorFlow Lite.
Deploy TensorFlow Lite models on various edge devices.
Utilize tools and techniques for model conversion and optimization.
Implement practical Edge AI applications using TensorFlow Lite.
This instructor-led live training in Lille (delivered online or on-site) is tailored for advanced professionals aiming to expand their knowledge of computer vision and utilize TensorFlow’s capabilities to develop advanced vision models via Google Colab.
By the conclusion of the program, attendees will have the ability to:
Develop and train convolutional neural networks (CNNs) using TensorFlow.
Leverage Google Colab for efficient, scalable cloud-based model creation.
Implement image preprocessing workflows for computer vision challenges.
Deploy computer vision solutions for real-world utility.
Improve CNN model accuracy by employing transfer learning.
Analyze and visualize the results generated by image classification models.
This instructor-led, live training in Lille (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.
By the end of this training, participants will be able to:
Set up and navigate Google Colab for deep learning projects.
Understand the fundamentals of neural networks.
Implement deep learning models using TensorFlow.
Train and evaluate deep learning models.
Utilize advanced features of TensorFlow for deep learning.
This instructor-led, live training in Lille (online or onsite) targets advanced professionals aiming to specialize in cutting-edge deep learning techniques for NLU.
By the end of this training, participants will be able to:
Understand the key differences between NLU and NLP models.
Apply advanced deep learning techniques to NLU tasks.
Explore deep architectures such as transformers and attention mechanisms.
Leverage future trends in NLU for building sophisticated AI systems.
This instructor-led live training in Lille (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.
By the end of this training, participants will be able to:
Understand the challenges of explainability in deep learning.
Implement advanced XAI techniques for neural networks.
Interpret decisions made by deep learning models.
Evaluate the trade-offs between performance and transparency.
This instructor-led, live training in Lille (online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision experts looking to expand their expertise 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.
This instructor-led live training in Lille (online or onsite) targets advanced professionals seeking to leverage AI techniques to revolutionize drug discovery and development processes.
By the end of this training, participants will be able to:
Understand the role of AI in drug discovery and development.
Apply machine learning techniques to predict molecular properties and interactions.
Use deep learning models for virtual screening and lead optimization.
Integrate AI-driven approaches into the clinical trial process.
This instructor-led, live training in Lille (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.
This instructor-led live training in Lille (online or onsite) targets developers from beginner to intermediate levels who wish to utilize 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.
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.
In this instructor-led live training in Lille, participants will master the most relevant and cutting-edge machine learning techniques in Python by building a series of demonstration applications that process image, music, text, and financial data.
By the end of this training, participants will be able to:
Implement machine learning algorithms and techniques to solve 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.
Utilize libraries and packages such as NumPy and Theano.
This hands-on training in Lille enables programmers to build AI models from the ground up using Python. You will develop proficiency in supervised learning, neural networks, and unsupervised techniques with scikit-learn and Apache Spark. The course emphasizes practical Jupyter exercises aimed at solving real-world problems.
This instructor-led training on Lille explores the theoretical underpinnings and practical application of Deep Reinforcement Learning using Python. Participants will design and train DRL agents using TensorFlow or PyTorch, leveraging algorithms like DQN and PPO to address complex, real-world challenges.
An introductory module in Lille that covers the basics of AI, ranging from intelligent agents to machine learning. It empowers leaders and architects to evaluate new AI trends, deploy practical solutions, and drive business agility through strategic automation.
Explore how Machine Learning and Deep Learning transform the automotive sector. This Lille course covers core concepts from simple automation to autonomous decision-making, including neural networks and practical TensorFlow examples for real-world applications.
This intensive three-day program in Lille provides a comprehensive overview of the theoretical underpinnings and practical applications of Artificial Neural Networks, Machine Learning, and Deep Learning. Participants will examine various network architectures, learning mechanisms, and mathematical foundations, progressing from basic perceptrons to sophisticated deep learning techniques.
This instructor-led, live training in Lille (online or on-site) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
By the end of this training, participants will be able to:
Apply core statistical methods to pattern recognition.
Use key models like neural networks and kernel methods for data analysis.
Implement advanced techniques for complex problem-solving.
Improve prediction accuracy by combining different models.
This instructor-led, live training in Lille (online or onsite) is tailored for software developers, data analysts, and technical professionals who wish to use TensorFlow 2.x and Keras to build, train, and deploy deep learning models for computer vision, natural language processing, and multimodal applications.
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
Audience
Developers
Engineers
Domain experts
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led live training in Lille (online or onsite) is designed for developers and data scientists who intend to utilize Tensorflow 2.x for building predictors, classifiers, generative models, and neural networks, among other applications.
Upon completion of this training, participants will be capable of:
Installing and configuring TensorFlow 2.x.
Understanding the advantages of TensorFlow 2.x over earlier versions.
Developing deep learning models.
Implementing an advanced image classifier.
Deploying deep learning models to the cloud, mobile applications, and IoT devices.
This 35-hour course in Lille explores the fundamentals of deep neural networks, covering CNNs, RNNs, and generative models such as GANs. Participants will gain practical experience with Theano and TensorFlow, learning to construct, train, and deploy production-grade deep learning models for real-world scenarios.
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Testimonials (6)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
The adaptation of exos to our context and the consideration of our request
Amel Guetat - EURO-INFORMATION DEVELOPPEMENTS
Course - Fraud Detection with Python and TensorFlow
Machine Translated
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
Magdalena - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data.
Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zaklad Uslugowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
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