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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: Mastering TensorFlow: From Getting Started to Practical Deep Learning Applications 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: Mastering TensorFlow: From Getting Started to Practical Deep Learning Applications 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: Mastering TensorFlow: From Getting Started to Practical Deep Learning Applications training is carried out through:
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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.
History and use cases of Deep Learning. Presentation of TensorFlow and installation. Understanding TensorFlow philosophy (Graphs, Sessions, Tensors). Using Google Colab and work environment. First simple linear regression model with TensorFlow.
Presentation of the Keras API integrated into TensorFlow. Designing an MLP (Multi-Layer Perceptron) for image classification (MNIST dataset). Data preparation: loading, normalization, splitting. Training, cross-validation, and overfitting. Performance monitoring with TensorBoard.
Advanced features: callbacks, saving and restoring models, transfer learning. Performance optimization and hyperparameter tuning. Export and deployment of models (TF Serving, TFLite). Introduction to image and text processing. Complete case study on a real project, from data preparation to deployment.
Target audience
Developers, data scientists, engineers, and researchers wishing to familiarize themselves with TensorFlow for Deep Learning
Prerequisites
Good understanding of Python and basic knowledge of machine learning
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