Implementation of all Loss Functions (Deep Learning) in NumPy, TensorFlow, and PyTorch
In this article, all common loss functions used in Deep Learning are discussed, and they are implemented in NumPy, PyTorch, and TensorFlow —  Contents Mean Squared Error (MSE) Loss Binary Cross-Entropy Loss Weighted Binary Cross-Entropy Loss Categorical Cross-Entropy Loss Sparse Categorical Cross-Entropy Loss Dice Loss KL Divergence Loss Mean Absolute Error (MAE) / L1 Loss Huber Loss Mean Squared Error (MSE) Loss
Implementation of all Loss Functions (Deep Learning) in NumPy, TensorFlow, and PyTorch In this article, all common loss functions used in Deep Learning are discussed, and they are implemented in NumPy, PyTorch, and TensorFlow —  Contents Mean Squared Error (MSE) Loss Binary Cross-Entropy Loss Weighted Binary Cross-Entropy Loss Categorical Cross-Entropy Loss Sparse Categorical Cross-Entropy Loss Dice Loss KL Divergence Loss Mean Absolute Error (MAE) / L1 Loss Huber Loss Mean Squared Error (MSE) Loss deep-learning
Implementation Loss Functions Deep Learning NumPy TensorFlow PyTorch article common loss function used Deep Learning discussed implemented NumPy PyTorch TensorFlow — Contents Mean Squared Error MSE Loss Binary CrossEntropy Loss Weighted Binary CrossEntropy Loss Categorical CrossEntropy Loss Sparse Categorical CrossEntropy Loss Dice Loss KL Divergence Loss Mean Absolute Error MAE L1 Loss Huber Loss Mean Squared Error MSE Loss deeplearning