About Course
Deep learning is a subfield of machine learning (ML) that focuses on the development and application of artificial neural networks to model and solve complex tasks. It is inspired by the structure and functioning of the human brain, with neural networks composed of interconnected nodes (artificial neurons) organized into layers.
Neural Networks
Deep learning models are typically built using artificial neural networks, which consist of an input layer, one or more hidden layers, and an output layer. Each layer contains nodes, and connections between nodes have associated weights that are learned during the training process.
Deep Neural Networks:
Depth: Deep learning models have multiple hidden layers, allowing them to learn hierarchical representations of data. The depth of these networks distinguishes them from traditional, shallow neural networks.
Training and Optimization:
Training Data: Deep learning models learn from labeled training data, adjusting their parameters to minimize the difference between predicted and actual outcomes.
Backpropagation: The backpropagation algorithm is commonly used to update the model’s weights and biases during training, optimizing its performance.
Popular Architectures:
Convolutional Neural Networks (CNNs): Well-suited for image recognition tasks, CNNs use convolutional layers to automatically learn features from input data.
Recurrent Neural Networks (RNNs): Effective for sequential data, RNNs use memory cells to process information in a sequential manner.
Applications:
Computer Vision: Deep learning has excelled in image and video analysis, enabling applications like image classification, object detection, and facial recognition.
Natural Language Processing (NLP): Deep learning powers language-related tasks such as sentiment analysis, language translation, and chatbots.
Speech Recognition: Deep learning models are used for converting spoken language into written text.
