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 advanced tensors and autograd in PyTorch to develop solid professional skills.
- Design and train convolutional neural networks (CNNs) tailored to real business cases.
- Implement recurrent models (RNN, LSTM) for sequence processing in certified training programs.
- Optimize PyTorch model performance with GPU and optimization techniques in an MLOps context.
- Deploy MLOps pipelines integrating PyTorch for smooth production rollout in business settings.
- Compare PyTorch and TensorFlow to select the optimal framework for professional projects.
- Develop transfer learning skills to accelerate machine learning 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 intermediate-level PyTorch mastery, your ML models stay slow and inefficient, driving training costs up 5x according to Gartner, with 70% of data scientists wasting time on autograd bugs.
Companies suffer 20% losses in predictive accuracy, directly hitting revenue—like the 40% of ML projects that fail due to the wrong framework.
Your career stalls against rising demand (300k ML jobs in Europe by 2025), risking forced career change.
Invest now in this certified training to lock in your PyTorch, TensorFlow, and MLOps expertise.
Program
Module 1PyTorch Fundamentals: Tensors, Autograd, and Datasets (Practical Exercises, First Models)
Discover multidimensional tensors and PyTorch's dynamic autograd through interactive exercises on real datasets like MNIST, configure custom DataLoaders for efficient training, implement your first fully connected network with SGD optimization, test the backward pass live, produce gradient visualizations and export basic models, all while comparing with TensorFlow for deeper professional understanding.
Module 2PyTorch Convolutional Networks: CNNs and Computer Vision (ResNet Models, Image Exercises)
Dive into CNNs with PyTorch by building architectures like LeNet and ResNet on CIFAR-10, apply 2D convolutions, pooling, and batch normalization, train on GPU with DataParallel, evaluate performance via accuracy and loss curves, optimize hyperparameters with grid search, generate prediction heatmaps, integrate data augmentation techniques for enterprise datasets, and deploy an image classifier ready for MLOps.
Module 3PyTorch Sequential Models: RNN, LSTM, and Transformers (Time Series, NLP)
Master RNNs and LSTMs in PyTorch for predicting time series on financial data, implement embeddings and attention mechanisms for NLP tasks like text classification, use TorchText for fast preprocessing, train with teacher forcing and scheduled sampling, visualize hidden states, compare with TensorFlow for optimal transfers, and produce robust predictive models suited to professional business needs.
Module 4PyTorch Optimization and MLOps: Tuning, Deployment (CI/CD Pipelines, Monitoring)
Optimize your PyTorch models with AdamW, learning rate schedulers, and early stopping on real cases, integrate TorchServe for scalable serving, set up MLOps pipelines with MLflow and Docker, monitor production metrics via Weights & Biases, manage model versions, test robustness with adversarial training, export to ONNX for TensorFlow interoperability, and prepare cloud-ready deployments to accelerate enterprise projects.
Module 5Advanced PyTorch Projects: Transfer Learning and GANs (Real Cases, Certifying Deliverables)
Apply PyTorch transfer learning from ImageNet to specific domains like object detection with YOLO, generate realistic images via conditional GANs, fine-tune pre-trained Hugging Face models, evaluate with FID scores and cross-validation, integrate MLOps for A/B testing, collaborate on an end-to-end team project, produce a portfolio of deployable models, and receive certification validating your professional PyTorch and ML ecosystem skills.
Evaluation method
- Daily interactive quizzes on PyTorch and MLOps concepts.
- Practical projects evaluated by experts (CNN, RNN, deployment).
- Final case study with personalized feedback and certification.
Learning method
- Hands-on methods with 70% practice on real PyTorch code.
- Case studies from companies using PyTorch and TensorFlow.
- Individual support in small groups (max 10 participants).
- Post-training resources: notebooks, videos, MLOps community.
Methods, materials and delivery
The Training PyTorch - Mastering Advanced ML 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 Training PyTorch - Mastering Advanced ML 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 Training PyTorch - Mastering Advanced ML 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.
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 - Mastering Advanced ML Models 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 - Mastering Advanced ML Models 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, machine learning basics, experience with NumPy and Pandas, familiarity with tensors via TensorFlow or equivalent.
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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