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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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The Training: Hyperparameter Optimization in Deep Learning with Optuna: Complete and Practical Guide training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training: Hyperparameter Optimization in Deep Learning with Optuna: Complete and Practical Guide training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training: Hyperparameter Optimization in Deep Learning with Optuna: Complete and Practical Guide training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Introduction to hyperparameter optimization: challenges, traditional methods vs. automatic. Presentation of Optuna, installation, architecture (studies, trials). Python interface. First practical project: optimization of a scikit-learn classifier.
Study of sampling algorithms (TPE, Random, CMA-ES). Early stopping methods (pruning) to save computation time. Practical exercises on optimizing deep learning models with Keras or PyTorch. Setting up custom callbacks to monitor trials.
Designing custom samplers and handling multiple constraints. Multi-objective studies and advanced visualization of results (importance, evolution, correlations). Integrating Optuna into industrial pipelines (storage with RDB, use with DVC/MLflow). Best practices, debugging, reproducibility, Q&A and real cases on client data.
Target audience
Data scientists, machine learning engineers, researchers and developers wishing to optimize their models effectively with Optuna
Prerequisites
Knowledge of supervised machine learning, Python, basics of deep learning (PyTorch or TensorFlow), experience training models
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