PyTorch Training - Develop High-Performance Deep Learning Models

$4,620

  • 4 days
  • Onsite
  • 10 people max.
Ref: VDL971
10 people max.
$4,620
In-company rate: from $7,865 for the whole team
On-site and certification exam on quote
4 days
Onsite

Don't let this gap widen

Why this program matters

  • Without mastering intermediate PyTorch, your deep learning models stagnate: trainings 3x slower without CUDA, production error rates at 25% due to lack of DataLoader optimizations, failed deployments costing 10k€ in project delays.

  • Competitors with TorchServe deploy 5x faster, capturing 40% AI market share.

  • Avoid invisible gradient bugs multiplying GPU costs x4, losses from poorly managed datasets, and obsolescence against scalable AI.

  • Invest 28 hours for 10x ROI gains in efficiency, stay ahead in ML.

Allan BUSI, Learni trainer · AI expert
Allan BUSI

Learni trainer · AI expert

73%productivity gap
×3cost of inaction
Flag of the United StatesBuilt for US teams
Payment by wire transferEnhanced confidentialityPerfect for companies with 50 to 500 employeesScheduling to fit any time zone

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41,181 programs · 25 countries

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  • Equans
  • EDF
  • Ubisoft
  • Microsoft
  • Atos

Every program built with a dedicated engineer

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Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft, Microsoft and Atos.

PyTorch training objectives

  • Master advanced tensors and PyTorch's autograd
  • Build and train complex CNNs and RNNs
  • Optimize performance with CUDA and DataParallel
  • Manage datasets and DataLoaders for large volumes
  • Deploy models to production via TorchServe
  • Profile and debug for 10x speed gains

Program

PyTorch curriculum: 4 modules over 4 days

Module 1 : Advanced 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 2 : Neural 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 3 : Optimization 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 4 : Deployment 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.

How PyTorch skills are assessed

  • Daily interactive quizzes
  • Assessed practical exercises
  • Certifying final project

How the PyTorch course is taught

  • 70% hands-on practice on real cases
  • 30% applied theory
  • Pair exercises

PyTorch training: access and practical arrangements

The PyTorch Training - Develop High-Performance Deep Learning Models 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 - Develop High-Performance Deep Learning Models 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 - Develop High-Performance Deep Learning Models 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.

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.

Our method

How Learni guarantees the quality of your PyTorch training

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.

Step 1

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.
Step 2

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.
Step 3

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.
Step 4

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.

A child walking to school with a backpack
Our social commitment

A school kit donated to a child for every training booked

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
Enroll and offer a kit
FAQ

PyTorch: frequently asked questions

Which tools are used in PyTorch Training training?+
Hands-on work runs on pyTorch tensors, hooks, nn.Module, torchVision, torch.optim and distributedDataParallel. Those tools come from the PyTorch Training program itself, delivered on site over 4 days, so participants practise on the stack they will use at work.
What can participants do after PyTorch Training training?+
They can master advanced tensors and PyTorch's autograd, build and train complex CNNs and RNNs and optimize performance with CUDA and DataParallel. Those are the stated objectives of the PyTorch Training program at Intermediate level, checked by the end-of-session assessment.
What does the PyTorch Training program cover, day by day?+
Day 1: Advanced Tensors and Autograd: manipulation, gradients, backprop (PyTorch tensors, hooks). Day 2: Neural Networks: CNN, RNN, transformers (nn.Module, TorchVision). Day 3: Optimization and Datasets: CUDA, DataLoaders, augmentations (torch.optim, DistributedDataParallel). Day 4: Deployment and Projects: TorchServe, ONNX, profiling (TorchScript, TensorBoard). The PyTorch Training program runs over 4 days, delivered on site.
How is PyTorch Training training assessed?+
Assessment relies on daily interactive quizzes, assessed practical exercises and certifying final project. Learni issues a completion certificate together with the individual assessment report at the end of the PyTorch Training program, which an L&D team can file as evidence.
How much does PyTorch Training training cost per participant?+
PyTorch Training training costs $4,620 per participant. For an in-company team, the flat rate starts at $7,865 for the whole group, up to 8 people. The detailed quote, sent within one business day, adjusts the amount to the format and how far the syllabus is tailored to your team.
How long does PyTorch Training training take?+
4 days, available live online (US time zones) or on-site at your offices. Half-day tracks spread over several weeks are possible so the team stays operational.
Who pays for PyTorch Training training?+
Most US teams pay directly through their company L&D or training budget. Learni invoices in US dollars and accepts bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
What do participants need to know before PyTorch Training training?+
Mastery of Python, basics in NumPy/Pandas, knowledge of deep learning and neural networks.
Is a certificate issued after PyTorch Training training?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report. Learni is a certified training provider and prepares for third-party certifications, but does not award them itself.
Does Learni provide the equipment for PyTorch Training training?+
No. A computer and a stable internet connection are required for each participant. Learni provides the learning platform, the trainer and all course materials.
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