Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training PyTorch - Master the basics of deep learning training are: Python basics (syntax, lists, functions) and elementary knowledge of machine learning. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training PyTorch - Master the basics of deep learning training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training PyTorch - Master the basics of deep lea... | Learni

The story of Learni

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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Methods, approaches, and teaching resources

The Training PyTorch - Master the basics of deep learning 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.

Teaching resources provided

To ensure the quality of the Training PyTorch - Master the basics of deep learning training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and 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.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training PyTorch - Master the basics of deep learning training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

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.

Training access terms and deadlines

Training objectives

Master PyTorch tensors and autograd for professional projects

Develop simple and efficient deep learning models in a business environment

Implement datasets and DataLoaders for certified training

Build and train convolutional neural networks with PyTorch

Optimize models for fast inference in production

Manage GPU/CPU environments for scalable AI skills

Apply PyTorch to real-world business cases with practical exercises

Training program

Module 1PyTorch Fundamentals: installation and tensor manipulation (conda environments, vector operations, practical exercises)

Quick installation of PyTorch with CUDA for GPU, creation and manipulation of multidimensional tensors, arithmetic operations and automatic transformations via autograd, exercises on real datasets like MNIST to visualize gradients, interactive debugging with Jupyter Notebooks, generation of loss graphs to analyze initial learning, all while building a simple ongoing project that evolves each day.

Module 2PyTorch Models: neural networks and datasets (nn.Module, DataLoader, SGD optimization)

Definition of custom model classes with nn.Module, loading datasets via torchvision and creating DataLoaders for efficient batching, implementation of a basic image classifier with fully connected layers, supervised training over multiple epochs with loss functions like CrossEntropyLoss, real-time monitoring of accuracy and precision metrics, collaborative exercises on CIFAR-10 classification, hyperparameter tuning to improve performance from the first practical session.

Module 3Advanced PyTorch Training: CNN and deployment (Torchvision, inference, saved models)

Building convolutional neural networks (CNNs) with Conv2D and pooling layers for object recognition, fine-tuning pre-trained models like ResNet, optimization with Adam and learning rate schedulers, evaluation on validation sets with confusion matrices, exporting models to TorchScript for deployment, inference testing on unseen data, finalization of the ongoing project with a complete report and optimized source code, ready for enterprise integration.