NVIDIA DLI Fundamentals of Deep Learning Training Course
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Description of the training: NVIDIA DLI Fundamentals of Deep Learning Training Course
Deep Learning is a branch of Machine Learning that uses deep neural networks (multiple hidden layers) to learn complex hierarchical representations from massive amounts of data. It powers the major recent advances in computer vision, natural language processing and generative AI.
What is the difference between Machine Learning and Deep Learning?Classic Machine Learning (regression, SVM, trees, random forest) requires manual feature engineering and works well on modest volumes. Deep Learning automatically learns features from raw data and excels on large datasets, at the cost of more computing power (GPU).
Do you need a GPU for Deep Learning?Yes, a GPU is virtually essential to train deep learning models at a reasonable pace. NVIDIA GPUs (with CUDA) accelerate training by a factor of 10 to 100 compared with CPUs. NVIDIA provides a cloud GPU environment during the session.
How long is the NVIDIA DLI Fundamentals of Deep Learning training?The MFE-IT training lasts 1 day (7 hours), in a fully tailored format with a maximum of 3 participants per session. It covers intensively: neural network principles, backpropagation, convolutions (CNNs), data augmentation, transfer learning and GPU training. An NVIDIA DLI certificate is awarded and 30 days of post-training support are included.
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Training delivered in French, original content in English. Our sessions are guaranteed from a single registrant, with no risk of postponement (except in cases of force majeure). A prior interview takes place between the participant and a company representative to fully take into account the participant’s profile (level, needs, professional context, challenges, etc.). Evaluation: during the training, the trainer assesses participants’ learning progress through quizzes, role-plays and practical exercises. Participants receive a certificate of achievement at the end of the training. This training is part of our catalogue. Discover our other AI training courses to fully harness the potential of machine learning, LLMs and generative AI.
Objectives of the training: NVIDIA DLI Fundamentals of Deep Learning Training Course
- By the end of this training, you will be able to:
- Understand the fundamental mechanisms of deep neural networks
- Train deep learning models on GPU for image classification
- Apply data augmentation techniques to improve model performance
- Leverage transfer learning to build high-performing models with little data
- Deploy neural networks for production inference
- Master the TensorFlow and PyTorch frameworks for model development
Prerequisites of the training: NVIDIA DLI Fundamentals of Deep Learning Training Course
Basic Python programming experience is required (variables, loops, functions). Elementary notions of linear algebra (vectors, matrices) and statistics are recommended. No prior experience in deep learning or GPUs is required. Because every participant is unique, a personalised interview with our expert allows us to design a training course perfectly aligned with their goals, level and professional challenges.
Target audience of the training: NVIDIA DLI Fundamentals of Deep Learning Training Course
This training is intended for the following professionals:
- Developers and software engineers wishing to discover deep learning
- Beginner data scientists looking to master neural network fundamentals
- Researchers and academics interested in practical GPU deep learning applications
- Technical leads and AI architects evaluating deep learning adoption in their projects
Detailed programme of the training: NVIDIA DLI Fundamentals of Deep Learning Training Course
Introduction to neural networks
- Fundamental principles of machine learning and deep learning
- Neural network architecture: layers, weights, biases and activation functions
- Training process: forward propagation, backpropagation and gradient descent
- The role of GPUs in accelerating model training
- Hands-on lab: setting up the cloud GPU environment and a first neural network
Training classification models
- Convolutional neural networks (CNNs) for computer vision
- Data preparation and image classification model training
- Performance evaluation and hyperparameter tuning
- Hands-on lab: building and training an image classifier
Data augmentation and transfer learning
- Data augmentation techniques to enrich training sets
- Transfer learning: reusing pre-trained models (ImageNet)
- Fine-tuning and adapting existing models to new tasks
- Strategies to improve model generalisation
- Hands-on lab: custom classification with transfer learning
Deployment and advanced use cases
- Preparing a model for production deployment
- Introduction to natural language processing with neural networks
- Object detection and image segmentation
- Best practices and next steps to deepen your deep learning skills
- Hands-on lab: deploying a model and final assessment for the NVIDIA DLI certificate
Course highlights
- Is a certifying NVIDIA DLI course on deep learning fundamentals
- Covers neural networks, training and deployment
- Delivers a 100% hands-on, GPU-based learning experience
- Awards a recognised NVIDIA certificate of competency
FAQ
What is Deep Learning?
Deep Learning is a branch of Machine Learning that uses deep neural networks (multiple hidden layers) to learn complex hierarchical representations from massive amounts of data. It powers the major recent advances in computer vision, natural language processing and generative AI.
What is the difference between Machine Learning and Deep Learning?
Classic Machine Learning (regression, SVM, trees, random forest) requires manual feature engineering and works well on modest volumes. Deep Learning automatically learns features from raw data and excels on large datasets, at the cost of more computing power (GPU).
Do you need a GPU for Deep Learning?
Yes, a GPU is virtually essential to train deep learning models at a reasonable pace. NVIDIA GPUs (with CUDA) accelerate training by a factor of 10 to 100 compared with CPUs. NVIDIA provides a cloud GPU environment during the session.
How long is the NVIDIA DLI Fundamentals of Deep Learning training?
The MFE-IT training lasts 1 day (7 hours), in a fully tailored format with a maximum of 3 participants per session. It covers intensively: neural network principles, backpropagation, convolutions (CNNs), data augmentation, transfer learning and GPU training. An NVIDIA DLI certificate is awarded and 30 days of post-training support are included.