Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training Keras - Master Advanced Deep Learning as an Expert training are: Advanced proficiency in Python, TensorFlow, PyTorch, and hands-on deep learning experience with real-world projects.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Keras - Master Advanced Deep Learning as an Expert training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training Keras - Master Advanced Deep Learning a... | Learni

The story of Learni

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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Methods, approaches, and teaching resources

The Training Keras - Master Advanced Deep Learning as an Expert 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.

Teaching resources provided

To ensure the quality of the Training Keras - Master Advanced Deep Learning as an Expert training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and 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.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training Keras - Master Advanced Deep Learning as an Expert training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

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.

Training objectives

Design complex neural network architectures with Keras

Optimize hyperparameters for maximum performance

Implement custom callbacks and advanced monitoring

Deploy scalable Keras models in production

Integrate Keras with TensorFlow Extended for end-to-end pipelines

Analyze and debug expert DL models

Master best practices for production-ready Keras code

Training program

Module 1Day 1: Advanced Keras Architectures (hybrid models, TensorFlow tools)

Dive into Keras to build hybrid CNN-RNN networks, use Sequential and Functional API on ImageNet datasets, perform practical transfer learning exercises with pre-trained ResNet, generate your first optimized model deliverables, test in real-time to boost your expert skills.

Module 2Day 2: Hyperparameter Optimization (Keras Tuner, Bayesian methods)

Master Keras Tuner to explore massive hyperparameter spaces, apply Bayesian algorithms and Random Search on concrete video classification cases, code automated scripts, analyze ROC-AUC metrics live, produce tuning reports that turn your models into performance champions.

Module 3Day 3: Custom Callbacks and Monitoring (TensorBoard, EarlyStopping)

Develop custom callbacks for advanced logging and dynamic pruning, integrate TensorBoard into Keras to visualize gradients and losses live, simulate overfitting scenarios on custom datasets, create interactive dashboards, deliver tools ready for your critical DL projects.

Module 4Day 4: Keras Model Deployment (TensorFlow Serving, ONNX, Docker)

Convert your Keras models into scalable services with TF Serving and ONNX, containerize via Docker for AWS cloud or Kubernetes, test batch inferences on GPU hardware, deploy secure REST APIs, obtain production-ready deliverables that accelerate your expert time-to-market.

Module 5Day 5: End-to-End Projects and Keras Best Practices (TFX, Model Security)

Put it all together in a capstone project on real-time anomaly detection, integrate TFX for MLOps pipelines, apply security against adversarial attacks, peer-to-peer code review, finalize portfolio with deployed models, leave with complete expertise to dominate deep learning in the enterprise.