Deep learning networks, also known as deep neural networks (DNNs), are a subset of artificial neural networks (ANNs) that consist of multiple layers of interconnected nodes, or neurons. These networks are characterized by their depth, as they have many hidden layers between the input and output layers. Deep learning has become a dominant technique in various machine learning and artificial intelligence applications due to its ability to automatically learn and represent complex patterns and features from data. Here are some key points about deep learning networks:

1. Neural Network Architecture: Deep learning networks are built upon the basic structure of neural networks, which consists of layers of neurons. These layers typically include an input layer, one or more hidden layers, and an output layer.
2. Multiple Hidden Layers: What distinguishes deep learning from shallow networks is the presence of multiple hidden layers. In deep networks, these hidden layers can number in the tens or even hundreds, allowing for the learning of hierarchical features and representations.
3. Feedforward Networks: Deep learning networks are often feedforward neural networks, where data flows from the input layer through the hidden layers to the output layer without loops or feedback. This architecture is suitable for tasks like image classification and natural language processing.
4. Activation Functions: Neurons in deep networks use activation functions, such as ReLU (Rectified Linear Unit), sigmoid, or tanh, to introduce non-linearity into the model, enabling it to approximate complex functions.
5. Backpropagation: Training deep learning networks typically involves the backpropagation algorithm, which adjusts the weights and biases of neurons to minimize a loss function. This process is iterative and relies on gradient descent optimization techniques.
6. Convolutional Neural Networks (CNNs): CNNs are a specialized type of deep learning network designed for image and spatial data. They use convolutional layers to automatically learn features like edges, textures, and patterns from images.
7. Recurrent Neural Networks (RNNs): RNNs are deep learning networks designed for sequential data, such as time series and natural language. They have loops that allow information to persist across time steps, making them suitable for tasks like speech recognition and text generation.
8. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU): These are variations of RNNs with improved ability to capture long-range dependencies in sequential data. They are widely used in natural language processing and speech recognition.
9. Autoencoders: Autoencoders are a type of deep learning network used for unsupervised learning and feature extraction. They aim to learn a compressed representation of input data and are used in dimensionality reduction and anomaly detection.
10. Applications: Deep learning networks have been applied to a wide range of applications, including image and video analysis, speech recognition, natural language processing, autonomous vehicles, recommendation systems, and healthcare, among others.
11. Deep Learning Frameworks: There are various deep learning frameworks and libraries available, such as TensorFlow, PyTorch, and Keras, that make it easier to design, train, and deploy deep learning models.