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.
Trusted by — schools, companies and industrial groups
Learning objectives
- Install and configure PyTorch with GPU.
- Manipulate tensors and autograd for automatic calculations.
- Build simple neural network models.
- Train and optimize models on real datasets.
- Evaluate performance with precise metrics.
- Deploy a basic PyTorch model in production.
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 training, waste 20h on failed PyTorch configs, like 70% of beginners who give up.
Your AI projects fail at 80% due to lack of solid foundations, losing business opportunities estimated at 50k€/year in data science.
Trained competitors deploy 3x faster, capturing exploding AI markets (+40% jobs 2024).
Risk debug overcosts at 125€/h freelance, or stay stuck on obsolete TensorFlow.
Invest 5 days, gain expertise valued at 15k€ salary boost.
Program
Module 1Day 1: PyTorch Installation and Essential Tensors (CUDA tools, vector operations, deliverables: interactive notebook)
Dive into PyTorch by installing the GPU environment, manipulate tensors for fast calculations, complete exercises on shapes and broadcasts, code your first gradients with autograd, test on MNIST datasets, and leave with a functional notebook ready to scale your AI projects.
Module 2Day 2: nn.Module and Basic Architectures (build methods, forward pass, deliverables: linear model)
Build your first nn.Module modules, assemble linear layers and ReLU activations, train a classifier on Fashion-MNIST using DataLoader, optimize with SGD and Adam, visualize losses in real-time, and apply to a concrete image classification case to boost your practical skills.
Module 3Day 3: CNN and PyTorch Convolutions (Conv2D tools, pooling, deliverables: convolutional network)
Master CNNs with Conv2d and MaxPool, code a network for CIFAR-10 object recognition, handle data augmentations with transforms, monitor training with TensorBoard, debug overfitting with dropout, and finish with a high-performing model on validation for immediate results.
Module 4Day 4: RNN, LSTM and Sequences (recurrent methods, backprop through time, deliverables: text predictor)
Explore RNN and LSTM for time series, process text with embeddings, train a sequence generator on IMDB reviews, handle masks and packing, evaluate perplexity, integrate into a concrete NLP project, and gain fluency in analyzing your own sequential data.
Module 5Day 5: Deployment and Advanced Projects (TorchScript, ONNX, deliverables: deployed app)
Optimize models with quantization, export to TorchScript for fast inference, test Flask deployment, integrate GPU inference, finalize a capstone project on a custom dataset, receive expert feedback, and leave as an expert ready to integrate PyTorch into your professional workflows.
Evaluation method
- Daily quizzes, practical exercises, graded final project, self-assessment of skills.
Learning method
- 70% hands-on practice on real cases, 30% interactive theory, pair coding exercises.
Methods, materials and delivery
The PyTorch Training - Master Deep Learning in 5 Days 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 Deep Learning in 5 Days 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 Deep Learning in 5 Days 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 Deep Learning in 5 Days training cost?+
The individual price is $5,775, and the team / on-site price is $10,106 (USD). A detailed quote is sent within one business day.
How long is the PyTorch Training - Master Deep Learning in 5 Days training?+
The training lasts 5 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, basics in NumPy, knowledge of linear algebra.
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
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