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Artificial Intelligence | AI on Instagram: "Gradient descent is a fundamental optimization algorithm used by most AI models to learn from data by minimizing a loss function, which measures how far the model’s predictions are from the true values. Conceptually, it treats the loss function as a landscape (we call this the loss landscape) with peaks and valleys representing high and low errors. At any point on this landscape, the gradient (vector of slopes) indicates the direction and steepness of

Artificial Intelligence | AI on Instagram: "Gradient descent is a fundamental optimization algorithm used by most AI models to learn from data by minimizing a loss function, which measures how far the model’s predictions are from the true values. Conceptually, it treats the loss function as a landscape (we call this the loss landscape) with peaks and valleys representing high and low errors. At any point on this landscape, the gradient (vector of slopes) indicates the direction and steepness of

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Triplet Loss - Contrastive Learning

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Hidden units | Neural network and deep learning |

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What is loss function?

What is loss function?

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Andrej Karpathy explains the process of deriving the gradient of the loss function with respect to the weights for optimization, using gradientdescent

Andrej Karpathy explains the process of deriving the gradient of the loss function with respect to the weights for optimization, using gradientdescent

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AI • Machine Learning • Tech on Instagram: "In a feedforward neural network (also known as an MLP), neurons are arranged in layers where each neuron receives inputs from the previous layer, multiplies them by corresponding weights, adds a bias term, and applies an activation function to produce its output. This process continues layer by layer until the final output is produced. When an example is passed through the network, the output is compared to the target value, and the difference is calcu

AI • Machine Learning • Tech on Instagram: "In a feedforward neural network (also known as an MLP), neurons are arranged in layers where each neuron receives inputs from the previous layer, multiplies them by corresponding weights, adds a bias term, and applies an activation function to produce its output. This process continues layer by layer until the final output is produced. When an example is passed through the network, the output is compared to the target value, and the difference is calcu

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Santosh Kumar on Instagram: "🔢Day 164– Data_Science_Journey(Deep Learning) 📊Topic :Classification- Loss Function and Cost Function Today I Explored loss function and cost function in deep learning and how to use classification in deep learning Classification 1.Binary Cross Entrophy / Log Loss : Binary cross-entropy (log loss) is a loss function used in binary classification problems. It quantifies the difference between the actual class labels (0 or 1) and the predicted probabilities output by

Santosh Kumar on Instagram: "🔢Day 164– Data_Science_Journey(Deep Learning) 📊Topic :Classification- Loss Function and Cost Function Today I Explored loss function and cost function in deep learning and how to use classification in deep learning Classification 1.Binary Cross Entrophy / Log Loss : Binary cross-entropy (log loss) is a loss function used in binary classification problems. It quantifies the difference between the actual class labels (0 or 1) and the predicted probabilities output by

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Artificial Intelligence | AI on Instagram: "Gradient descent is a fundamental optimization algorithm used by most AI models to learn from data by minimizing a loss function, which measures how far the model’s predictions are from the true values. Conceptually, it treats the loss function as a landscape (we call this the loss landscape) with peaks and valleys representing high and low errors. At any point on this landscape, the gradient (vector of slopes) indicates the direction and steepness of

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First Principles AI on Instagram: "Have you wondered why we use the negative log function for a loss function? #datascience #machinelearning #deeplearning #techeducation #aiexplained #nlp #mathematics"

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AI • Machine Learning • Tech on Instagram: "Gradient descent is an optimization algorithm widely used in machine learning to minimize a loss function, which is a measure of how well a model’s predictions match the actual outcomes. In the gradient descent process, the model iteratively adjusts its parameters (its weights and biases) to reduce the loss. The parameters are adjusted based on the gradient, or partial derivatives, of the loss function with respect to each parameter. The gradient point

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Dadzie TV on Instagram: "Watch how broadcaster Serwaa Amihere, music legend Kojo Antwi, actor and politician Fred Nuamah, and media personality KOD arrived at the residence of the late Daddy Lumba to commiserate with the bereaved family. Their solemn expressions and quiet exchanges reflected the deep sense of loss shared by fans and loved ones of the legendary musician."

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AI • Machine Learning • Tech on Instagram: "A neural network “learns” by adjusting its parameters (weights and biases) by increasing or decreasing them numerically so that the network can make increasingly more accurate predictions. The output is compared to the actual target values using a loss function, which quantifies the prediction error. To minimize the error, the network performs backpropagation, an algorithm that computes the gradient (set of all partial derivatives) of the loss with res

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Artificial Intelligence | AI on Instagram: "A neural network “learns” by adjusting its parameters (weights and biases) by increasing or decreasing them numerically so that the network can make increasingly more accurate predictions. The output is compared to the actual target values using a loss function, which quantifies the prediction error. To minimize the error, the network performs backpropagation, an algorithm that computes the gradient (set of all partial derivatives) of the loss with res

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Artificial Intelligence | AI on Instagram: "Autoencoders use loss functions to help train the encoder and decoder. The encoder can take many forms of data, but let’s say it takes an image, like one from the MNIST dataset. The image is passed through the encoder to compress it into a simpler form. The decoder then tries to recreate the original image from that compressed version. A loss function, like Mean Squared Error (MSE), compares the original image with the reconstructed one. MSE takes the

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AI • Machine Learning • Tech on Instagram: "Gradient descent is a fundamental optimization algorithm used by most AI models to learn from data by minimizing a loss function, which measures how far the model’s predictions are from the true values. Conceptually, it treats the loss function as a landscape (we call this the loss landscape) with peaks and valleys representing high and low errors. At any point on this landscape, the gradient (vector of slopes) indicates the direction and steepness of

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Artificial Intelligence | AI on Instagram: "Gradient descent is a fundamental optimization algorithm used by most AI models to learn from data by minimizing a loss function, which measures how far the model’s predictions are from the true values. Conceptually, it treats the loss function as a landscape (we call this the loss landscape) with peaks and valleys representing high and low errors. At any point on this landscape, the gradient (vector of slopes) indicates the direction and steepness of

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YogSattva™| Manisha Patil on Instagram: "ABC Juice Benefits: Glowing and clear skin Boosts immunity Improves digestion & detoxifies body Supports weight loss Enhances brain function Improves heart health Sharpens eyesight Balances hormones (good for PCOS) Strengthens hair & nails Increases energy levels"

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Artificial Intelligence | AI on Instagram: "Autoencoders use loss functions to help train the encoder and decoder. The encoder can take many forms of data, but let’s say it takes an image, like one from the MNIST dataset. The image is passed through the encoder to compress it into a simpler form. The decoder then tries to recreate the original image from that compressed version. A loss function, like Mean Squared Error (MSE), compares the original image with the reconstructed one. MSE takes the

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Neural AI on Instagram: "Gradient descent is a fundamental optimization algorithm used by most AI models to learn from data by minimizing a loss function, which measures how far the model’s predictions are from the true values. Conceptually, it treats the loss function as a landscape (we call this the loss landscape) with peaks and valleys representing high and low errors. At any point on this landscape, the gradient (vector of slopes) indicates the direction and steepness of the fastest increas

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Alex Zhang on Instagram: "A "sparse categorical cross entropy loss function" is a variant of the standard cross-entropy loss used in multi-class classification tasks, where the true labels are provided as integer values representing the class index directly, rather than as one-hot encoded vectors, making it ideal for situations where labels are not one-hot encoded; essentially, it internally converts the integer labels to one-hot format before calculating the cross-entropy loss."

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