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PyTorch Training - Develop High-Performance Deep Learning Models

Ref: VDL971
10 people max.
$5,280 HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$300/day onsite · +$540 with certification exam
4 days
Remote

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Learning 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

The Learni story

Founded by passionate learning and innovation experts, Learni's mission is to make professional training accessible to everyone, anywhere in the world. Our team operates in major hubs — London, New York, Boston — and internationally, to support talents and organizations in upskilling.

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
Allan Busi

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

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.

Evaluation method

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

Learning method

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

Methods, materials and delivery

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 eligible for corporate training budgets and employer-funded plans.

Verified reviews

What our learners

4.9 · +100 verified reviews
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
Read all reviews
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.

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.

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