L'deep learning, Or deep learning is a sub-discipline of artificial intelligence and machine learning that focuses on the use of multi-layered artificial neural networks (called deep neural networks) to model and solve complex problems.
Inspired by the structure and function of the human brain, deep learning enables machines to learn from large quantities of unstructured data (images, text, sound) by automatically extracting characteristics (features) without the need for human intervention.
This technology has revolutionised sectors such as healthcare, finance and transport, thanks to its ability to handle tasks that were previously inaccessible to traditional algorithms.
How it works
1. Artificial Neural Networks (ANN)
Artificial neural networks are made up of layers of neurons interconnected :
- Input layer Receives raw data (words, pixels, frequencies, etc.).
- Hidden layers transform the data successively using weight and bias adjusted during training.
- Output layer Final classification: produces the final prediction or classification.
2. Forward Propagation
The data passes from the input layer to the output layer via the hidden layers. Each neuron applies an activation function (such as ReLU, sigmoid, tanh) to introduce non-linearity.
3. Backpropagation
After forward propagation, the error between the prediction and the ground truth is calculated. This error is then backpropagated through the network to adjust the weights and biases using optimisation algorithms such as the gradient descent.
4. Training and Iterations
The forward propagation and backpropagation process is repeated over many iterations (epochs) until the model converges to an optimal performance.
Types of Deep Learning
- Supervised learning
- Uses labelled data to train the model.
- Applications: Image classification, speech recognition.
- Unsupervised learning
- Uses unlabelled data to discover underlying structures.
- Applications: Clustering, dimensionality reduction.
- Reinforcement Learning
- The model learns by trial and error as it interacts with an environment.
- Applications : Video games, autonomous robots.
- Semi-supervised learning
- Combines labelled and unlabelled data.
- Applications: Image recognition with little labelled data.
Areas of Application of Deep Learning
1. Computer Vision
- Image and Face Recognition
- Object Detection
- Image Segmentation
2. Natural Language Processing (NLP)
- Automatic Translation
- Feelings Analysis
- Text generation
3. Voice recognition
- Virtual Assistants (e.g. Siri, Alexa)
- Automatic Transcription
4. Games and Simulations
- Artificial Intelligence in Video Games
- Simulation of Complex Behaviour
5. Health and Medicine
- AI-assisted diagnosis
- Medical Imaging Analysis
6. Automotive
- Autonomous Vehicles
- Driving Assistance Systems
7. Finance
- Fraud detection
- Algorithmic Trading
8. Industry and Robotics
- Predictive Maintenance
- Process Automation
9. Security
- Intelligent Surveillance
- Intrusion detection
Applications of Deep Learning
- Facial Recognition Used by smartphones for unlocking and by security systems for surveillance.
- Autonomous Cars Tesla, Waymo and others are using neural networks to navigate and make decisions in real time.
- Vocal Assistants Siri, Alexa and Google Assistant interpret and respond to voice commands using deep learning models.
- Medical diagnosis Analysis of medical images (MRI, X-rays) to detect anomalies such as tumours.
- Automatic Translation Google Translate and other services are continually improving the accuracy of their translations thanks to deep learning.
- Video Games Advanced AI for more realistic non-player character behaviour.
Advantages and Limitations of Deep Learning
Benefits
- High Performance
- Excellent ability to process and analyse large quantities of complex data.
- Automatic Feature Learning
- Ability to automatically extract relevant characteristics without human intervention.
- Flexibility and adaptability
- Applicability to a variety of domains and data types.
- Continuous Improvement
- Models can be improved as data and computing resources increase.
- Unstructured Data Management
- Efficient for processing images, text, videos, etc.
Limits
- Need for Large Quantities of Data
- Large datasets are needed to train high-performance models.
- Computational resources
- Computing power and memory requirements, which are often costly.
- Interpretability Low
- Models are often seen as "black boxes", making it difficult to explain the decisions taken.
- Overfitting
- Risk of over-fitting to training data, reducing generalisation.
- Dependence on Quality Data
- Sensitivity to noisy or biased data, which can affect performance.
Deep Learning Framework
1. Development Environment
- Popular Frameworks :
- TensorFlow Developed by Google, widely used for research and production.
- PyTorch Developed by Facebook, popular for its flexibility and use in search.
- Keras High-level API based on TensorFlow, simplifying model creation.
2. Tools and Libraries
- CUDA NVIDIA's parallel computing platform, essential for GPU acceleration.
- CuDNN Primitives library for neural networks optimised for GPUs.
- Scikit-learn Library for complementary machine learning tasks.
3. Cloud Computing Platforms
- Google Cloud AI Platform
- AWS Deep Learning AMIs
- Microsoft Azure Machine Learning
4. Development Methodologies
- Data Management : Preparation, cleaning and augmentation of data.
- Model Architecture Choice of network types (CNN, RNN, Transformers, etc.).
- Training and validation Data division, choice of hyperparameters.
- Deployment Integrating models into applications or services.
Trends and the Future of Deep Learning
Current trends
- Multimodal models
- Integration of different data sources (text, image, sound) in the same model.
- Self-supervised learning
- Techniques that reduce the need for labelled data by exploiting the internal structures of the data.
- Model Optimisation
- Development of lighter, more efficient models for execution on devices with limited resources (edge computing).
- Interpretability and Explicability
- Research into ways of making model decisions more transparent and understandable.
- Ethics and Regulation
- Consideration of bias, data confidentiality and the ethical implications of AI.
Future prospects
- Improved Interpretability
- Development of techniques to better understand and explain neural network decisions.
- Advanced Generative AI
- Models capable of creating high-quality original content (images, text, music).
- Integration with other technologies
- Synergy with the Internet of Things (IoT), augmented/virtual reality (AR/VR) and blockchain.
- Model Design Automation
- Use of theAutoML to automate the selection and design of neural network architectures.
- Large-scale deployment
- Pervasive integration of deep learning into everyday and industrial applications.
📊 Figures and Statistics in France and Worldwide
France
- Investments :
- In 2024, France has invested around 2 billion euros in AI research and development, with a significant proportion earmarked for deep learning.
- Start-ups and companies :
- More than 500 startups French companies are active in the field of AI and deep learning, in a variety of sectors including healthcare, finance and the automotive industry.
- Industrial Adoption :
-
- Approximately 60% of large French companies are integrating deep learning solutions into their business processes.
-
World
- Global AI market :
- The global AI market is expected to reach 1,500 billion dollars by 2025, with deep learning accounting for a major part of this growth.
- Investment in R&D :
- The United States and China dominate investment in AI, with combined budgets of nearly 70% of global spending on deep learning research.
- Industrial use :
- 80 % of Fortune 500 companies are using deep learning technologies to optimise operations, improve customer service and develop new products.
- Technological innovations :
- Significant advances in neural network architectures, such as the Transformers and generative neural networksThese new technologies will power new applications and improve existing performance.
