Deep Learning §

Group: 4 #group-4

Relations §

• Computer Vision: Deep Learning, particularly Convolutional Neural Networks, has revolutionized Computer Vision and enabled significant advancements in various tasks.
• Convolutional Neural Networks: Convolutional neural networks are a type of deep neural network commonly used for processing grid-like data such as images.
• Reinforcement Learning: Deep reinforcement learning combines deep neural networks with reinforcement learning algorithms.
• Computer Vision: Deep learning has achieved remarkable success in computer vision tasks such as image recognition and object detection.
• Convolutional Neural Networks: Convolutional Neural Networks are a type of Deep Learning model
• Backpropagation: Backpropagation is a crucial algorithm in deep learning, enabling the training of deep neural networks.
• Machine Learning: Deep Learning is a subset of Machine Learning that uses deep neural networks with many layers to learn from data in a hierarchical manner.
• Big Data: Deep learning models often require large amounts of data for training, making big data an important enabler.
• Self-Supervised Learning: Self-supervised learning is a technique for training deep learning models on unlabeled data by creating pretext tasks.
• Generative Adversarial Networks: Generative adversarial networks (GANs) are a type of deep learning architecture used for generating new data samples.
• Transformers: Transformer models like BERT and GPT are a type of deep learning architecture that has achieved state-of-the-art results in natural language processing.
• Artificial Intelligence (AI): Deep Learning is a subset of Machine Learning that uses multi-layered Neural Networks to learn and make intelligent decisions from large amounts of data.
• Activation Functions: Activation functions are essential components of deep learning models like deep neural networks.
• Attention Mechanism: The attention mechanism is a key component of transformer models that allows them to focus on relevant parts of the input data.
• Overfitting: Overfitting is a common challenge in deep learning, where models perform well on training data but poorly on new data.
• Natural Language Processing: Deep Learning techniques, such as neural networks, have revolutionized NLP and enabled significant advances in various tasks.
• Regularization: Regularization techniques like dropout and L1/L2 regularization help prevent overfitting in deep learning models.
• Transfer Learning: Transfer learning involves using pre-trained deep learning models as a starting point for new tasks, reducing training time and data requirements.
• Artificial Intelligence: Deep Learning is a subset of machine learning that uses artificial neural networks with multiple layers to learn from data in a hierarchical manner.
• Explainable AI: Explainable AI aims to make deep learning models more interpretable and transparent, which is important for high-stakes applications.
• Generative Adversarial Networks: GANs are a type of deep learning model.
• Machine Learning: Deep learning is a technique within the broader field of machine learning.
• Backpropagation: Backpropagation is a key algorithm used to train deep neural networks by adjusting weights based on error gradients.
• Recurrent Neural Networks: Recurrent neural networks are a type of deep neural network designed to process sequential data such as text or time series.
• Machine Learning: Deep Learning is a subset of Machine Learning that uses multi-layered neural networks to learn from data in a hierarchical manner.
• Artificial Neural Networks: Deep learning is a subfield of machine learning that uses artificial neural networks as the primary model architecture.
• Narrow AI: Deep learning, a subset of machine learning, is a powerful technique used in many narrow AI applications.
• Natural Language Processing: Deep learning models like transformers and recurrent neural networks are widely used in natural language processing tasks.
• Ethical AI: As deep learning systems become more prevalent, there is a growing focus on developing ethical AI principles and practices.
• Neural Networks: Deep learning is a subfield of machine learning that uses deep neural networks with multiple layers to learn hierarchical representations of data.
• Gradient Descent: Gradient descent is an optimization algorithm used to update the weights of deep neural networks during training.