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 Advanced PyTorch Training - Master Professional Deep Learning training are: Mastery of Python, PyTorch basics, deep learning and neural modeling experience.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Advanced PyTorch Training - Master Professional Deep Learning training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Advanced PyTorch Training - Master Professional… | 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 Advanced PyTorch Training - Master Professional 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 Advanced PyTorch Training - Master Professional 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 Advanced PyTorch Training - Master Professional 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 objectives

Implement advanced CNN and RNN architectures with PyTorch

Optimize hyperparameters using automated techniques

Develop GANs and VAEs for data generation

Master transfer learning on large datasets

Deploy models in production with TorchServe

Analyze performance and debug complex networks

Create end-to-end pipelines for real-world AI projects

Training program

Module 1Day 1: Advanced PyTorch Architectures (CNN, RNN, TorchVision tools)

Dive into convolutional CNNs and recurrent RNNs, configure datasets with DataLoader, train initial models on real cases like image classification, generate TensorBoard visualizations, produce performance reports to validate insights, experience the thrill of seeing your networks converge quickly.

Module 2Day 2: Optimization and Transfer Learning (AdamW methods, schedulers)

Fine-tune pre-trained ResNet models on your data, apply learning rate schedulers, test augmentation techniques, measure gains on real benchmarks, iterate via hyperparameter search with Optuna, leave with optimized production-ready scripts, boost your projects starting tomorrow.

Module 3Day 3: Generative GANs and VAEs (torch.nn, probabilistic distributions)

Build adversarial GANs to synthesize realistic images, implement variational VAEs with KL-divergence, experiment on FashionMNIST datasets, stabilize training with Wasserstein loss, generate stunning visual deliverables, master the art of creative AI generation.

Module 4Day 4: Deployment and Scaling (TorchServe, ONNX, distributed training)

Convert models to ONNX for interoperability, deploy via TorchServe on servers, scale with DataParallel on multi-GPU, test real latencies, integrate Flask APIs for web apps, produce deployable Docker containers, transition seamlessly from lab to production.

Module 5Day 5: Advanced Projects and Optimization (profiling, quantization)

Develop an end-to-end capstone project on a real client challenge, profile with Torch Profiler for bottlenecks, apply INT8 quantization, benchmark 2-3x accelerations, present a certifying portfolio, consolidate skills via peer-review, leave as an expert ready to conquer the AI industry.