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 the fundamentals of PyTorch to develop deep learning skills suited to the business environment.
- Build and train simple neural models during this certified professional training.
- Implement tensors and autograd to optimize machine learning computations.
- Develop convolutional neural networks with PyTorch in a professional context.
- Set up basic MLOps pipelines to deploy models in production.
- Acquire practical PyTorch skills for real-world business projects.
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 PyTorch mastery, 70% of data jobs require these skills according to LinkedIn, leaving novices behind amid heightened competition where PyTorch experts earn 25% more in salary.
Companies lose up to 500k€ annually on delayed ML projects due to lack of internal skills, risking obsolescence against agile competitors.
Your career stagnates without these essential deep learning foundations, missing opportunities in generative AI and MLOps.
Invest now to secure your professional future and boost your team's performance.
Program
Module 1PyTorch Fundamentals: Installation, Tensors, and Initial Manipulations (Anaconda, Jupyter Environment)
Discover PyTorch through quick installation with Anaconda and Conda, set up your Jupyter environment for interactive notebooks, manipulate multidimensional tensors with vectorized operations, complete practical exercises on shapes and data types, visualize tensors with Matplotlib, and create your first functional scripts to solidify deep learning basics for business.
Module 2Autograd and PyTorch Training Loops: Automatic Gradients and Optimizers (torch.optim)
Dive into PyTorch's Autograd system to automatically compute gradients, implement complete training loops with datasets like MNIST, configure optimizers like SGD and Adam, monitor losses with built-in metrics, perform hands-on exercises on linear regression, and generate evolution graphs to analyze model convergence for beginners.
Module 3PyTorch Neural Models: nn.Module, Layers, and Activation Functions (ReLU, softmax)
Build your first fully connected networks using the nn.Module class, integrate linear layers and activation functions like ReLU or Sigmoid, train classifiers on Fashion-MNIST datasets, debug with TorchSummary to visualize architectures, complete guided practical sessions on image classification, and export saved models ready for professional machine learning applications.
Module 4CNNs with PyTorch: Convolutions, Pooling, and Transfer Learning (torchvision, ResNet)
Master convolutional networks using Conv2D and MaxPooling in PyTorch, apply data augmentation with torchvision.transforms, fine-tune pre-trained models like ResNet on CIFAR-10, evaluate performance with accuracy and confusion matrices, conduct practical workshops on object recognition, and produce analysis reports to simulate business use cases.
Module 5Introductory PyTorch MLOps: Deployment, Monitoring, and Integration (TorchServe, MLflow)
Get started with MLOps practices using PyTorch by containerizing models with Docker, deploy with TorchServe for fast inferences, track experiments with MLflow, integrate basic metrics monitoring, test on real REST API cases, and conclude with a deliverable capstone project ready for professional production deployment.
Evaluation method
- Interactive quizzes at the end of each day to validate theoretical knowledge.
- Final practical project on a real dataset using PyTorch.
- Qualiopi certification issued after evaluation of acquired skills.
Learning method
- Hands-on learning with 70% practical exercises on PyTorch.
- Real-world case studies from businesses to contextualize skills.
- Individualized feedback from expert machine learning trainers.
- Post-training resources: notebooks, videos, and dedicated community.
Methods, materials and delivery
The Training PyTorch - Master the Basics of 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 Training PyTorch - Master the Basics of 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 Training PyTorch - Master the Basics of 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 Training PyTorch - Master the Basics of Deep Learning training cost?+
The price is $5,775 (USD) per participant. A detailed quote is sent within one business day.
How long is the Training PyTorch - Master the Basics of Deep Learning 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?+
Solid foundations in Python programming, basic knowledge of linear algebra and statistics, no prior experience in deep learning.
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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