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 TensorFlow - Master deep learning in production training are: Mastery of Python, basics in machine learning and deep learning, familiarity with NumPy and Pandas.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training TensorFlow - Master deep learning in production training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training TensorFlow - Master deep learning in pr... | 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 TensorFlow - Master deep learning in production 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 TensorFlow - Master deep learning in production 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 TensorFlow - Master deep learning in production 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 access terms and deadlines

Training objectives

Master the high-level Keras API in TensorFlow 2.x

Build and train advanced convolutional neural networks

Optimize models for production with TensorFlow Extended

Deploy scalable AI applications on the cloud

Implement regularization techniques and data augmentation

Analyze performance with TensorBoard and profilers

Training program

Module 1Advanced TensorFlow Fundamentals: Keras API, Datasets, and Callbacks

Dive into TensorFlow 2.x by setting up the environment on Colab and Jupyter, load real datasets like CIFAR-10 via tf.data for efficient pipelines, configure callbacks for automatic saving and early stopping, train your first CNN on a virtual GPU, visualize learning curves with TensorBoard, and generate an initial performance report to build confidence.

Module 2Convolutional Networks: Custom CNNs and Transfer Learning

Design advanced CNN architectures with ResNet and EfficientNet, apply transfer learning from ImageNet pre-trained models to custom images, integrate data augmentation via ImageDataGenerator for robustness, test on real-world cases like object detection, optimize hyperparameters with Keras Tuner, and deploy a functional prototype ready for validation.

Module 3Recurrent Networks: LSTM, GRU, and Time Series

Explore RNNs for sequence processing with LSTM and GRU on datasets like IMDB or financial time series, implement attention mechanisms for intelligent focus, address vanishing gradients with advanced techniques, train on real stock predictions, evaluate with RMSE and BLEU metrics, and generate live predictions that impress with their accuracy.

Module 4Optimization and Deployment: TF Serving, TFLite, and Cloud

Transition to production by quantizing models for mobile with TensorFlow Lite, deploy via TF Serving on Docker for fast inference, integrate TensorFlow Extended for end-to-end pipelines, test scalability on AWS SageMaker or GCP AI Platform, monitor drifts with TensorBoard, and deliver a deployed model accessible via secure REST API.

Module 5Advanced Projects: GANs, RL, and Real-World Team Cases

Synthesize skills by building a GAN for realistic image generation on custom datasets, introduce reinforcement learning with TF-Agents on Gym environments, collaborate in pairs on a fictional client project like speech recognition, debug performance with profilers, present your final certified deliverable, and leave with a professional portfolio for AI missions.