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.
Learning objectives
- Master advanced tensors and Autograd for efficient computations.
- Build CNNs and RNNs using nn.Module and TorchVision.
- Optimize training using DataLoader, optimizers, and GPU.
- Implement custom models with hooks and callbacks.
- Deploy models to production using TorchScript.
- Analyze and debug networks with TensorBoard.
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 training, your PyTorch models risk costly Autograd bugs, leading to 40% time lost in debugging, sub-optimal GPU performance reducing efficiency by 30%, and unstable deployments causing business losses of thousands of euros.
Imagine missing AI opportunities due to non-scalable DataLoaders, or stagnating on poorly optimized CNNs in the face of competition.
70% of intermediate data scientists abandon projects due to lack of PyTorch mastery, according to Stack Overflow studies.
Avoid these pitfalls: reach pro level in 4 days, secure your career, and boost project ROI by 200%.
Program
Module 1PyTorch Fundamentals: Tensors, Autograd, Modules (Exercises, First Models)
Manipulate multidimensional tensors, compute automatic gradients with Autograd, create custom nn.Module modules, through interactive exercises on MNIST datasets, test on GPU, produce a functional image classifier ready for training, gain immediate fluency.
Module 2PyTorch Convolutional Models: CNN, TorchVision (Real Cases, Transfer Learning)
Design advanced CNN architectures, integrate TorchVision for ready datasets, apply transfer learning on ResNet, train on real images like CIFAR-10, visualize activations, generate accurate predictions, boost your visual projects in just a few exciting iterations.
Module 3PyTorch Sequences and Optimization: RNN, LSTM, DataLoader (Scalable Training)
Implement RNN and LSTM for time series, configure DataLoader with augmentations, optimize with Adam and schedulers, manage overfitting with dropout, test on IMDb texts, deliver a high-performing NLP model, accelerate your training workflows by 50%.
Module 4PyTorch Deployment and Debugging: TorchScript, TensorBoard (Final Project)
Convert models to TorchScript for production, profile with TensorBoard, debug hooks and gradients, deploy Flask API, integrate ONNX, complete an end-to-end object detection project, leave with a concrete portfolio and deployable skills.
Evaluation method
- Daily practical quizzes, final certifying project, individual feedback from the trainer.
Learning method
- 70% practical exercises, 30% applied theory, real business cases.
Methods, materials and delivery
The PyTorch Training - Master 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 - Master 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 - Master 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 - Master High-Performance Deep Learning training cost?+
The individual price is $4,620 (USD). A detailed quote is sent within one business day.
How long is the PyTorch Training - Master 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?+
Mastery of Python, basics in NumPy/Pandas, knowledge of deep learning fundamentals, and beginner experience in PyTorch or 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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