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Neural networks are inspired by the structure and functioning of the human brain. The brain is composed of neurons, which communicate with each other through synapses. Similarly, neural networks consist of artificial neurons (also called nodes or units) connected by weights. Neural networks are designed to recognize patterns. They can be used for tasks such as classification, regression, and more complex tasks like image recognition, natural language processing, and game playing. This is the first layer of the network.
How it works?
Weighted Sum:Each neuron receives inputs from the previous layer, each of which has an associated weight. The neuron computes a weighted sum of these inputs. A probability distribution, or a numerical value, depending on the task.
Activation Function:The weighted sum is then passed through an activation function, which introduces non-linearity into the network. Common activation functions include:
Sigmoid:Maps input to a value between 0 and 1.
ReLU (Rectified Linear Unit): Outputs the input directly if it’s positive; otherwise, it outputs zero.
Tanh:Maps input to a value between -1 and 1.
Loss Function:This function measures the difference between the network’s output and the actual target. Common loss functions include Mean Squared Error (for regression tasks) and Cross-Entropy Loss (for classification tasks). This is the method used to update the weights. The network calculates the gradient of the loss function with respect to each weight and adjusts the weights in the opposite direction of the gradient (this is known as gradient descent).
A framework where two neural networks, a generator and a discriminator, are trained simultaneously. The generator tries to create data that looks real, while the discriminator tries to distinguish between real and fake data. When a model performs well on training data but poorly on unseen data. Techniques like regularization, dropout, and cross-validation are used to mitigate this. Neural networks have many hyperparameters, like the number of layers, the number of neurons in each layer, the learning rate, etc. Tuning these hyperparameters is crucial for achieving good performance.
Advanced topics
A framework where two neural networks, a generator and a discriminator, are trained simultaneously. The generator tries to create data that looks real, while the discriminator tries to distinguish between real and fake data. When a model performs well on training data but poorly on unseen data. Techniques like regularization, dropout, and cross-validation are used to mitigate this. Neural networks have many hyperparameters, like the number of layers, the number of neurons in each layer, the learning rate, etc.
Summary:Neural networks are powerful tools that can model complex patterns in data. They have a wide range of applications, from image recognition to game playing. The field is constantly evolving, with new architectures and techniques being developed to improve performance and efficiency. When a model performs well on training data but poorly on unseen data. Techniques like regularization, dropout, and cross-validation are used to mitigate this. Neural networks have many hyperparameters, like the number of layers, the number of neurons in each layer, the learning rate, etc. Tuning these hyperparameters is crucial for achieving good performance. Training deep neural networks can be computationally expensive, often requiring GPUs.
