Ever caught yourself reaching for your phone, telling yourself it’s “just for a minute”… and then suddenly it’s midnight?You didn’t mean to spend the last hour watching cat videos, political debates, or oddly satisfying cleaning reels. But somehow, you did. Again. Don’t worry — it’s not just you.It’s your brain….
Describe the scene using three vivid sensory details — one for sight, one for sound, and one for touch. Summarize the mood of the image in one powerful sentence without naming any objects. Write a poetic description of the image focusing only on colors and emotions. Imagine the main subject…
Dimensionality reduction is the process of reducing the number of features (variables) in a dataset while preserving important information. It helps in: ✅ Reducing computational cost (faster processing)✅ Removing noise from data✅ Avoiding overfitting✅ Visualizing high-dimensional data Types of Dimensionality Reduction 1. Feature Selection (Selecting important features) 2. Feature Extraction…
The tanh function, short for hyperbolic tangent function, is another commonly used activation function in neural networks. It maps any real-valued number into a value between -1 and 1. This function is similar to the sigmoid function but offers some advantages that make it more suitable for certain applications. Mathematical…
Initializing weights and biases is a crucial step in building a neural network. Proper initialization helps ensure that the network converges to a good solution and does so efficiently. Let’s explore the reasons in detail: 1. Breaking Symmetry If all weights are initialized to the same value (e.g., zeros), then…
The sigmoid function is one of the most commonly used activation functions in neural networks, especially in binary classification tasks. It maps any real-valued number into a value between 0 and 1, making it suitable for models that need to output probabilities. Mathematical Definition The sigmoid function, also known as…
An activation function is a mathematical function applied to the output of each neuron in a neural network. It determines whether a neuron should be activated or not based on its input. Activation functions introduce non-linearity into the network, allowing it to model complex patterns and interactions in the data….
What are Biases in Neural Networks? Biases are additional parameters in neural networks that are added to the weighted sum of inputs to a neuron before applying the activation function. They help the model to fit the data better by providing an additional degree of freedom. Role of Biases How…
Defining weights in a neural network involves initializing them to appropriate values before training begins. Proper initialization is critical for ensuring efficient and effective training. Here’s a step-by-step guide on how to define weights in a neural network: 1. Understanding Weight Initialization Weights are the parameters that connect neurons between…