1. Self-Supervised Learning A training method where a model learns to solve a task without explicit supervision. It is widely used in natural language processing and computer vision applications.
2. Hybrid Model Integration A technique that combines multiple deep-learning models to solve complex problems. It is used in image and speech recognition applications.
3. Generative Adversarial Networks (GANs) A type of deep learning model that can generate new data that is similar to the training data. They are used in image and video generation applications.
4. Neuroscience-Based Deep Learning A technique that models the structure and function of the human brain. It is used in natural language processing and robotics applications.
5. Use of Edge Intelligence A technique where deep learning models are deployed on edge devices like smartphones, IoT devices, & autonomous vehicles. It is used in real-time image & speech recognition apps.
6. Deep Diving Using Convolutional Neural Networks CNNs have revolutionized computer vision by automatically learning & extracting features from images. CNNs are used in image classification, object detection, & face recognition.
7. High-Performance NLP modelsThese are deep learning models that are designed to process & understand human language. They are used in language translation, sentiment analysis, and chatbot applications.
8. Multimodal Multitasking Transformers These are Deep learning models that can process multiple modalities of data such as text, image, and speech. Used to solve problems in autonomous driving, healthcare, & more.
9. AutoML A technique where deep learning models are automatically designed & optimized using machine learning algorithms. Used in applications like image & speech recognition.
10. Vision Transformer A new trend that uses transformer architecture, commonly used in NLP, for image processing. Has shown promising results in image classification & is quickly gaining popularity.