Delivered either online or on-site, our instructor-led live Neural Network training sessions use interactive discussion and hands-on practice to demonstrate the construction of Neural Networks. We utilize a range of primarily open-source toolkits and libraries, while also leveraging the computational power of advanced hardware (GPUs) and optimization techniques involving distributed computing and big data. These courses are built on widely used programming languages such as Python, Java, and R, and incorporate powerful libraries including TensorFlow, Torch, Caffe, and Theano. The curriculum balances theoretical concepts with practical implementation, covering various neural network architectures such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).
Neural Network training is offered as either "online live training" or "onsite live training". Online live training (also known as "remote live training") is conducted via an interactive remote desktop. Onsite live training can be provided either directly 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 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 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 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 (3)
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
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
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