CIFAR-10 Image Classification with CNN (PyTorch) - Complete Guide CIFAR-10 Image Classification Using CNN with PyTorch This comprehensive guide demonstrates how to classify images from the CIFAR-10 dataset using Convolutional Neural Networks (CNNs) built with PyTorch. The project covers the entire machine learning pipeline from data loading to model deployment, including advanced techniques for improving performance. 🚀 Key Features Complete PyTorch implementation Data augmentation techniques Multiple CNN architectures Training visualization Model evaluation metrics Hyperparameter tuning Model saving/loading Deployment options 📊 Performance Baseline model: ~70% accuracy Improved mod...
Advanced Radial Basis Function Networks with PyTorch Advanced Radial Basis Function Networks (RBFNs) with PyTorch Introduction to RBF Networks Radial Basis Function Networks are a type of artificial neural network that uses radial basis functions as activation functions. They are particularly effective for pattern recognition and function approximation problems. Key Characteristics Three-layer architecture : Input layer, hidden RBF layer, and linear output layer Localized activation : Each neuron in the hidden layer responds only to inputs near its center Fast training : Often requires fewer iterations than multilayer perceptrons Universal approximation : Can approximate any continuous function given enough hidden units Mathematical Foundation : The RBF network implements a function of the form: f(x) = Σ w_i * φ(||x - c_i||) ...
LSTM Text Generation with PyTorch - Complete Guide LSTM Text Generation with PyTorch A comprehensive guide to building word-level language models with Long Short-Term Memory networks Introduction to LSTM Text Generation Long Short-Term Memory (LSTM) networks are a special kind of recurrent neural network (RNN) capable of learning long-term dependencies. They are particularly useful for sequence prediction problems like text generation, where the context from previous words is crucial for predicting the next word. Key Concepts Word-level modeling : Predicts the next word given previous words Embeddings : Dense vector representations of words ...
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