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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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The Master PyTorch: Intensive Professional Training for Deep Learning Experts training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Master PyTorch: Intensive Professional Training for Deep Learning Experts training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Master PyTorch: Intensive Professional Training for Deep Learning Experts training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Discovery of the PyTorch ecosystem, advanced tensor manipulation, automatic differentiation, principles of sequential and functional models. Practical hands-on exercises.
Custom model definition, use of torch.nn and torch.optim, training and evaluation techniques, fine-grained Dataset and DataLoader management for data augmentation and complex pipelines.
Implementation of CNN, RNN, LSTM, Transformers, and GAN. Introduction to transfer learning and fine-tuning with pre-trained models. Case studies on vision and NLP.
Multi-GPU optimization with DataParallel and DistributedDataParallel, GPU memory management, effective debugging, quantization and export (TorchScript, ONNX), integration into APIs.
Design, training, and deployment of a complete deep learning model on an industrial use case (vision, NLP, or audio). Group presentation, robustness tests, documentation, and MLOps best practices.
Target audience
Engineers, data scientists, developers, and researchers wishing to leverage PyTorch for advanced deep learning projects.
Prerequisites
Mastery of Python, knowledge of machine learning and deep learning basics.
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