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.
Training TensorFlow - Master deep learning in production
Trusted by — schools, companies and industrial groups
41,182 programs · 25 countries
Every program built with a dedicated engineer
Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft, Microsoft and Atos.
TensorFlow 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
Program
TensorFlow curriculum: 5 modules over 5 days
Module 1 : Advanced 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 2 : Convolutional 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 3 : Recurrent 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 4 : Optimization 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 5 : Advanced 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.
How TensorFlow skills are assessed
- Daily quizzes, practical projects each day, final exam with capstone project, personalized feedback.
How the TensorFlow course is taught
- 30% theory / 70% practice alternation, exercises on real datasets, live collaborative coding.
TensorFlow training: access and practical arrangements
The Training TensorFlow - Master deep learning in production program is delivered onsite or remote (blended-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 TensorFlow - Master deep learning in production 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 TensorFlow - Master deep learning in production 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
Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.
The Learni story
Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.
Our method
How Learni guarantees the quality of your TensorFlow training
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.
Our social commitment
A school kit donated to a child for every training booked
To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.
- Backpack, notebooks and essential supplies
- Distributed through our partner charities
- Included, at no extra cost to you
FAQ
TensorFlow: frequently asked questions
What can participants do after TensorFlow training?+
They can master the high-level Keras API in TensorFlow 2.x, build and train advanced convolutional neural networks and optimize models for production with TensorFlow Extended. Those are the stated objectives of the TensorFlow program at Intermediate level, checked by the end-of-session assessment.
What does the TensorFlow program cover, day by day?+
Day 1: Advanced TensorFlow Fundamentals: Keras API, Datasets, and Callbacks. Day 2: Convolutional Networks: Custom CNNs and Transfer Learning. Day 3: Recurrent Networks: LSTM, GRU, and Time Series. Day 4: Optimization and Deployment: TF Serving, TFLite, and Cloud. Day 5: Advanced Projects: GANs, RL, and Real-World Team Cases. The TensorFlow program runs over 5 days, delivered on site.
How is TensorFlow training assessed?+
Assessment relies on daily quizzes, practical projects each day, final exam with capstone project, personalized feedback. Learni issues a completion certificate together with the individual assessment report at the end of the TensorFlow program, which an L&D team can file as evidence.
How much does TensorFlow training cost per participant?+
TensorFlow training costs $5,775 per participant. For an in-company team, the flat rate starts at $10,395 for the whole group, up to 8 people. The detailed quote, sent within one business day, adjusts the amount to the format and how far the syllabus is tailored to your team.
How long does TensorFlow training take?+
5 days, available live online (US time zones) or on-site at your offices. Half-day tracks spread over several weeks are possible so the team stays operational.
Who pays for TensorFlow training?+
Most US teams pay directly through their company L&D or training budget. Learni invoices in US dollars and accepts bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
What do participants need to know before TensorFlow training?+
Mastery of Python, basics in machine learning and deep learning, familiarity with NumPy and Pandas.
Is a certificate issued after TensorFlow training?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report. Learni is a certified training provider and prepares for third-party certifications, but does not award them itself.
Does Learni provide the equipment for TensorFlow training?+
No. A computer and a stable internet connection are required for each participant. Learni provides the learning platform, the trainer and all course materials.
Your professional training, anywhere
Let's build your TensorFlow learning path.
30 minutes with a learning advisor. No commitment. No sales pitch dressed up as a demo.
Reply within 24h · Vetted experts · USD invoicing · W-9 available
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