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.
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.
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 this upskilling, your team accumulates a technological gap that translates directly into productivity loss.
Organizations that don't train their talents on key topics see their competitiveness drop.
Every quarter without training is a gap widening with competitors who invest.
The cost of inaction quickly exceeds that of well-targeted training.
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, e-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
Learni programs are available inter-company and intra-company, onsite or remote. Enrollments are possible up to 48 business hours before the program starts. Our programs are eligible for corporate funding paths. Contact us to discuss your training project and funding options.
Verified reviews
What our learners
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
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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