Deep Learning Training Paris

Master PyTorch, TensorFlow and neural networks to build high-performance AI models for production

5 daysAdvancedBlended (in-person + remote)

In Paris, our cybersecurity training supports Île-de-France businesses in their secure digital transformation. Organizations like Thales, Capgemini, Atos trust our expertise to train their teams. Based in Station F, we understand the specific challenges of the France market and adapt our programs to local realities.

Key Information

Duration5 days

ModeBlended (in-person + remote)

LevelAdvanced

LocationParis

Guide

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Deep Learning Production Guide

From PyTorch to production: MLOps, containerization and deep learning model deployment.

Guide

Presentation

Master PyTorch, TensorFlow and neural networks to build high-performance AI models for production

Objectives

Build and train neural networks with PyTorch and TensorFlow
Implement CNN, RNN, LSTM and Transformer architectures
Optimize hyperparameters and prevent overfitting
Deploy models to production with MLOps
Apply transfer learning and fine-tuning

Target Audience

→Data Scientists wanting to specialize in deep learning
→ML Engineers looking to deepen their skills
→Python developers interested in AI
→Researchers and PhD students in computer science
→Data Architects and AI Engineers

Prerequisites

Strong Python fundamentals (object-oriented programming). Math foundations (linear algebra, probability). Experience with NumPy and Pandas recommended.

Detailed Program

•Perceptrons, activation functions and backpropagation
•Introduction to PyTorch: tensors, autograd, modules
•TensorFlow/Keras: sequential and functional models
•GPU computing with CUDA

Frequently Asked Questions

Prerequisites

Strong Python fundamentals (object-oriented programming). Math foundations (linear algebra, probability). Experience with NumPy and Pandas recommended.

Target Audience

→Data Scientists wanting to specialize in deep learning
→ML Engineers looking to deepen their skills
→Python developers interested in AI
→Researchers and PhD students in computer science
→Data Architects and AI Engineers

Detailed Curriculum

Module 1: Deep Learning Fundamentals

→Perceptrons, activation functions and backpropagation
→Introduction to PyTorch: tensors, autograd, modules
→TensorFlow/Keras: sequential and functional models
→GPU computing with CUDA

Module 2: Computer Vision (CNN)

→Convolutions, pooling and classic architectures (LeNet, VGG, ResNet)
→Transfer learning with pretrained models
→Object detection: YOLO, Faster R-CNN
→Image segmentation and industrial applications

Module 3: NLP and Sequences (RNN, Transformers)

→RNN, LSTM and GRU for time series
→Attention mechanism and Transformer architecture
→BERT, GPT and pretrained language models
→Fine-tuning for text classification and NER

Module 4: MLOps and Production

→Experimentation with MLflow and Weights & Biases
→Model containerization (Docker, ONNX)
→Cloud deployment (AWS SageMaker, GCP Vertex AI)
→Model monitoring and A/B testing

Module 5: Practical Project

→Business problem definition and data collection
→Model training and optimization
→Deployment on REST API
→Documentation and best practices

Expected Outcomes

Build and train neural networks with PyTorch and TensorFlow

Implement CNN, RNN, LSTM and Transformer architectures

Optimize hyperparameters and prevent overfitting

Deploy models to production with MLOps

Apply transfer learning and fine-tuning

Companies in Paris using this training

Thales - Awareness training for 500+ employees
Capgemini - Ongoing certification program
Atos - Security audit and custom training
Station F startups - Monthly group training sessions

Regulatory Compliance

GDPR compliance, NIS2, LPM (Military Programming Law), PASSI (Information System Security Audit Provider), HDS hosting (Health Data Hosting), RGS (General Security Framework)

FAQs

What is the difference between PyTorch and TensorFlow?

PyTorch, developed by Meta, is preferred in research for its flexibility and eager execution mode. TensorFlow (Google) dominates in production thanks to TensorFlow Serving and TensorFlow Lite. Our training covers both frameworks to make you versatile.

Do I need a GPU for the training?

No, we use Google Colab Pro and AWS SageMaker Studio which provide free or low-cost cloud GPUs. For advanced exercises, we provide GPU instances. No special hardware is required.

Does this training prepare for a certification?

This hands-on training prepares you for TensorFlow Developer (Google) and AWS Machine Learning Specialty certifications. The concepts covered also align with requirements to become a Deep Learning Specialist on major cloud platforms.

What types of projects can I build after this training?

You will be able to develop computer vision systems (object detection, OCR), NLP models (chatbots, sentiment analysis), recommendation systems, and time series prediction models. The final project will allow you to create a complete solution deployed in production.

Ready to get started?

Next session in Paris

March 7, 2026