Rigorous trainer selection
Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.
- Triple validation: technical, pedagogical, sectoral.
- Minimum rating 4.8/5 over the last 12 sessions.
1 seat held for the next session
30:00Held for another
Request a call back and an advisor reaches you within 24 business hours, Monday to Saturday.
Trusted by — schools, companies and industrial groups
Every program built with a dedicated engineer
Learning objectives
- Master advanced tensors and autograd in PyTorch for rapid development
- Develop complex neural network architectures in a business context
- Optimize deep learning model training with professional techniques
- Implement certified MLOps pipelines integrating PyTorch
- Design scalable and production-deployable ML applications
- Acquire certified PyTorch skills to boost your career
Our social commitment
A school kit donated to a child for every training
To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.
- Backpack, notebooks and essential supplies
- Distributed through our partner charities
- Included, at no extra cost to you
The Learni story
Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.
Don't let this gap widen
Why this program matters
Without this upskilling, your team accumulates a technological gap that translates directly into productivity loss.
Organizations that don't train their talents on key topics see their competitiveness drop.
Every quarter without training is a gap widening with competitors who invest.
The cost of inaction quickly exceeds that of well-targeted training.
Program
Module 1PyTorch Foundations: Advanced Tensors and Autograd (practical exercises on real datasets)
Discover multidimensional tensors and PyTorch's autograd system through interactive exercises on datasets like MNIST and CIFAR-10, implement custom gradients, build optimized DataLoaders for fast training, analyze memory performance with built-in tools, and produce your first functional forward pass models at the end of the day to consolidate intermediate foundations.
Module 2PyTorch Architectures: CNN and RNN for Vision and Sequences (real business cases)
Dive into CNNs with Conv2D and pooling layers in PyTorch for image classification, develop RNNs and LSTMs for textual sequence processing, apply transfer learning on pre-trained ResNets, test on real cases like object detection, optimize hyperparameters via grid search, and generate visualizations with TensorBoard to evaluate architectures in a professional context.
Module 3Advanced PyTorch Training: Optimizers and Regularization (deliver optimized models)
Master optimizers like AdamW and SGD with momentum in PyTorch, implement dropout and batch normalization to avoid overfitting, configure dynamic learning rate schedulers, train on GPU with DataParallel, monitor metrics via wandb, apply early stopping on large datasets, and deploy a fine-tuned model ready for production with precise performance evaluation.
Module 4MLOps with PyTorch: Deployment and Monitoring (end-to-end certified pipelines)
Integrate PyTorch with MLOps tools like TorchServe and MLflow for scalable deployment, containerize models with Docker, automate CI/CD pipelines via GitHub Actions, monitor in production with Prometheus, manage model versions, test real-time inference on FastAPI endpoints, and complete a full deliverable project demonstrating certified enterprise skills.
Evaluation method
- Interactive quizzes and MCQs at the end of each day on PyTorch
- Final project: development of a deployed deep learning model
- Qualiopi certificate with assessment of acquired skills
Learning method
- 70% hands-on with exercises on Jupyter and Colab
- Real-world use cases tailored to professional needs
- Personalized pedagogical support in small groups
- Unlimited access to post-training resources
Methods, materials and delivery
The PyTorch Training - Mastering High-Performance Deep Learning program is delivered onsite or remote (blended-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.
The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.
Equipment required
For the smooth delivery of the PyTorch Training - Mastering High-Performance Deep Learning program, the following equipment is required:
- Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
- Training environments installed on workstations or accessible online
- Course materials, hands-on exercises and complementary resources
- Post-training access to materials and educational resources
For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.
* contact us for remote delivery feasibility** ratio varies depending on the program
Skills assessment methods
Assessment of skills acquired during the PyTorch Training - Mastering High-Performance Deep Learning program is performed through:
- During training: case studies, hands-on labs and professional scenarios
- End of training: self-assessment questionnaire and skills evaluation by the trainer
- After training: completion certificate detailing acquired skills
Program accessibility
Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.
Enrollment terms and lead times
Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.
Our method
Training quality, guaranteed at every step
Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.
You meet the trainer beforehand
30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.
- Live briefing on goals and team context.
- Veto right — we swap the trainer for free if needed.
Content tailored to your context
No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.
- Hands-on cases drawn from your stack and projects.
- Program co-written then validated by your team.
Continuous quality follow-up
Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.
- NPS, knowledge quizzes and skills self-assessment.
- Satisfaction guarantee: fully satisfied or free rework.
A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.
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FAQ
Frequently asked questions
How much does the PyTorch Training - Mastering High-Performance Deep Learning training cost?+
The individual price is $4,620 (USD). The team / on-site group package is shown on the course page. A detailed quote is sent within one business day.
How long is the PyTorch Training - Mastering High-Performance Deep Learning training?+
The training lasts 4 journées, available live online (US time zones) or on-site at your offices.
How is this training paid for?+
Most US teams pay directly through their company (L&D or training budget). We invoice in US dollars and accept bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
Are there any prerequisites?+
Proficiency in Python, basic knowledge of machine learning and NumPy, experience with frameworks like TensorFlow
Is a certificate delivered at the end?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report.
Does Learni provide the equipment?+
No. A computer and stable internet connection are required for the participant. Learni provides the educational platform, the trainer and all course materials.
On-site & remote
This training across cities
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