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What are building blocks of deep networks, elaborate.

What are building blocks of deep networks, elaborate.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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**What are the building blocks of deep networks, elaborate.** Deep networks, also known as deep neural networks, are composed of several essential building blocks that enable them to learn complex patterns and representations from data. These building blocks include: 1. **Neurons (Nodes):** The fundamental units of a neural network, inspired by biological neurons. Each neuron receives input, processes it, and passes on the output to the next layer. Neurons apply an activation function to introduce non-linearity. 2. **Layers:** - **Input Layer:** The first layer that receives raw data. Each neuron in this layer represents a feature of the input data. - **Hidden Layers:** Intermediate layers between input and output that perform transformations and extract features. The depth of a network corresponds to the number of these layers. - **Output Layer:** The final layer that produces the network's prediction or outcome. 3. **Weights and Biases:** Parameters that the network learns during training. Weights determine the strength of the connection between neurons, while biases adjust the weighted sum of inputs before applying the activation function. 4. **Activation Functions:** Functions applied to the output of neurons to introduce non-linearity. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, and tanh. 5. **Loss Function:** A function that measures how well the network’s predictions match the actual data. The goal of training is to minimize the loss function by adjusting the weights and biases. 6. **Optimization Algorithm:** Algorithms like Gradient Descent and its variants (e.g., Adam, RMSprop) that update the weights and biases to minimize the loss function. 7. **Backpropagation:** The process of computing the gradient of the loss function with respect to the network’s parameters and updating them accordingly. It involves propagating errors backward through the network. By iteratively adjusting the weights and biases based on the loss gradient, deep networks can model complex data patterns and improve their predictions over time.
Transcribed Image Text:**What are the building blocks of deep networks, elaborate.** Deep networks, also known as deep neural networks, are composed of several essential building blocks that enable them to learn complex patterns and representations from data. These building blocks include: 1. **Neurons (Nodes):** The fundamental units of a neural network, inspired by biological neurons. Each neuron receives input, processes it, and passes on the output to the next layer. Neurons apply an activation function to introduce non-linearity. 2. **Layers:** - **Input Layer:** The first layer that receives raw data. Each neuron in this layer represents a feature of the input data. - **Hidden Layers:** Intermediate layers between input and output that perform transformations and extract features. The depth of a network corresponds to the number of these layers. - **Output Layer:** The final layer that produces the network's prediction or outcome. 3. **Weights and Biases:** Parameters that the network learns during training. Weights determine the strength of the connection between neurons, while biases adjust the weighted sum of inputs before applying the activation function. 4. **Activation Functions:** Functions applied to the output of neurons to introduce non-linearity. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, and tanh. 5. **Loss Function:** A function that measures how well the network’s predictions match the actual data. The goal of training is to minimize the loss function by adjusting the weights and biases. 6. **Optimization Algorithm:** Algorithms like Gradient Descent and its variants (e.g., Adam, RMSprop) that update the weights and biases to minimize the loss function. 7. **Backpropagation:** The process of computing the gradient of the loss function with respect to the network’s parameters and updating them accordingly. It involves propagating errors backward through the network. By iteratively adjusting the weights and biases based on the loss gradient, deep networks can model complex data patterns and improve their predictions over time.
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