What Is Loss Function in Deep Learning - Search Videos

AI • Machine Learning • Tech on Instagram: "OpenAI’s CLIP (Contrastive Language–Image Pretraining) model is a multimodal neural network trained to connect text and images in a shared vector space. Instead of learning to classify images into fixed categories, CLIP learns representations by matching images with their corresponding text descriptions, optimizing so that the correct pairs have high similarity while mismatched pairs have low similarity. Both text and images are encoded into high-dimen

AI • Machine Learning • Tech on Instagram: "OpenAI’s CLIP (Contrastive Language–Image Pretraining) model is a multimodal neural network trained to connect text and images in a shared vector space. Instead of learning to classify images into fixed categories, CLIP learns representations by matching images with their corresponding text descriptions, optimizing so that the correct pairs have high similarity while mismatched pairs have low similarity. Both text and images are encoded into high-dimen

273.5K views3 weeks ago

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

Triplet Loss - Contrastive Learning

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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

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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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AI | Machine Learning | Tech on Instagram: "Machine learning and deep learning models such as neural networks process input data as arrays of numbers (1D, 2D, 3D, etc.), where each number represents a specific feature of the data. For example, a black and white image may get fed into the network as a matrix with values ranging from 0 to 1, the brightness of a pixel. These matrices are passed through many layers, each containing mathematical operations like matrix multiplications, where the input

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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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Artificial Intelligence | AI on Instagram: "Backpropagation utilizes the chain rule of calculus to compute the gradient of the loss function with respect to each weight in the network. The chain rule allows the decomposition of the gradient into a series of simpler, local gradients that can be efficiently calculated layer by layer, from the output layer back to the input layer. C: @3blue1brown #machinelearning #deeplearning #math #datascience"

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Freedom With AI on Instagram: "Follow & Comment “Nvidia” to unlock access to Nvidia’s Deep Learning Institute! 🚀 🧠 Learn Data Science, Generative AI, and more—all for FREE with courses designed by Nvidia’s top experts. 💡 Did you know? Their recent stock surge is proof that AI is the future, and you can be a part of it! 📍 Day 322 of 365 Days Freedom With AI Challenge #Nvidia #DeepLearningInstitute #AIRevolution #UpskillNow #DataScience"

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Santosh Kumar on Instagram: "🔢Day 163– Data_Science_Journey(Deep Learning) 📊Topic : Loss Function and Cost Function Today I Explored loss function and cost function in deep learning and how to use regression and classification in deep learning *Loss Function :- A way to calculate Error / diff bewteen predicted and acutal -Jab mai indivisual JaKe error ko calculate karta hu -loss function *Cost Function : Pure Batch ke lye calculate karta hai wo hai cost function *Regression 1. Mean Square Erro

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Santosh Kumar on Instagram: "🔢Day 160– Data_Science_Journey(Deep Learning) 📊Topic : Forward and Backward Propagation Today , I Explored forward and backward propagation topic understanding with an example like Perceptron *Forward propagation : Forward propagation is the fundamental process in a neural network where input data passes through multiple layers to generate an output. It is the process by which input data passes through each layer of neural network to generate output. *Backward prop

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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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AI • Machine Learning • Tech on Instagram: "How do neural networks actually learn? They use weights and biases that are initially randomized but are tweaked during the training process. This involves the chain rule and partial derivatives of the loss function with respect to weights and biases. Join our AI community for more posts like this @aibutsimple 烙 #machinelearning #datascience #deeplearning #math #neuralnetwork"

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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

154.9K views2 months ago

Ashish Singh on Instagram: "Watching my loss function chase the local minimum... The grind never ends. #ChasingTheMinimum #MachineLearningLife #LocalMinimaTrap #ModelTrainingStruggles #AIChase #MLFails #BioinformaticsJourney #DataScienceDiaries #DeepLearningDrama #NeuralNetProblems #machinelearningalgorithms #bioinformatics #biotechnology"

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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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