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 PyTorch Training - Develop High-Performance Deep Learning Models training are: Mastery of Python, basics in NumPy/Pandas, knowledge of deep learning and neural networks.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the PyTorch Training - Develop High-Performance Deep Learning Models training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

PyTorch Training - Develop High-Performance Deep... | 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 PyTorch Training - Develop High-Performance Deep Learning Models 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 PyTorch Training - Develop High-Performance Deep Learning Models 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 PyTorch Training - Develop High-Performance Deep Learning Models 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 program

Module 1Advanced Tensors and Autograd: manipulation, gradients, backprop (PyTorch tensors, hooks)

Dive into PyTorch's multidimensional tensors, manipulate them via interactive exercises on broadcasting and reshaping, implement autograd to compute gradients automatically, test on real MNIST datasets, create your first custom modules, produce loss graphs and validate with reusable concrete deliverables.

Module 2Neural Networks: CNN, RNN, transformers (nn.Module, TorchVision)

Build CNNs with convolutions and pooling via TorchVision, train on CIFAR-10 with practical exercises, advance to RNNs and LSTMs for time series, integrate attention mechanisms, optimize hyperparameters in real-time, generate predictions on concrete cases like image recognition, export functional models for your portfolio.

Module 3Optimization and Datasets: CUDA, DataLoaders, augmentations (torch.optim, DistributedDataParallel)

Accelerate training on GPU with CUDA and torch.cuda, configure DataLoaders for massive batching and shuffling, apply transformations via torchvision.transforms on photo-realistic exercises, test Adam/Warmup optimizers, measure 5x speedup on benchmarks, debug common bottlenecks, deliver scalable optimized scripts.

Module 4Deployment and Projects: TorchServe, ONNX, profiling (TorchScript, TensorBoard)

Convert models to TorchScript for fast inference, deploy via TorchServe on Docker servers, export to ONNX for interoperability, profile with TensorBoard to identify bottlenecks, complete a full capstone project on object detection, test in production conditions, leave with a deployed API and impact report.