Part 1: The PyTorch Foundation
Dec 15, 2025
Master PyTorch fundamentals - tensors, autograd, and gradient descent. Learn dynamic computation graphs, GPU acceleration, and build your first neural network from scratch.
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Part 2: Deep Learning with PyTorch
Dec 17, 2025
Build production-ready neural networks with torch.nn, advanced optimizers, and efficient data pipelines. Train a complete CNN for image classification following industry best practices.
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Understanding Feed-Forward and Backpropagation: The Heart of Neural Networks
Dec 20, 2025
Learn how neural networks learn through feed-forward and backpropagation from scratch to truly understand these concepts.
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Understanding Activation Functions: The Non-Linear Magic in Neural Networks
Dec 23, 2025
Master activation functions from Sigmoid to GELU, understand why neural networks need non-linearity and how to choose the right activation for your model.
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Understanding Loss Functions: A Complete Guide from Theory to Practice
Dec 26, 2025
Master loss functions from MSE to Focal Loss with comprehensive theory, implementation, and practical guidance for choosing the right loss.
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Batch and Layer Normalization: Stabilizing Deep Neural Network Training
Dec 30, 2025
Master batch and layer normalization techniques that revolutionized deep learning training, with comprehensive theory, mathematical foundations, and practical implementations.
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Understanding Optimizers: How Neural Networks Actually Learn
Jan 03, 2026
Master optimization algorithms from SGD to Adam and beyond. Understand how neural networks navigate the loss landscape to find optimal solutions.
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Dropout: The Elegant Regularization Technique That Revolutionized Deep Learning
Jan 06, 2026
Master dropout regularization from theory to implementation, understanding why randomly dropping neurons during training produces remarkably robust neural networks.
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Weight Initialization: The Critical First Step in Neural Network Training
Jan 09, 2026
Master weight initialization techniques that determine whether your neural network trains successfully or fails before it even begins.
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Understanding Convolutional Neural Networks: From Pixels to Patterns
Jan 13, 2026
Dive into CNNs and learn how neural networks process visual information through convolution, pooling, and hierarchical feature learning.
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Understanding Recurrent Neural Networks: A Complete Guide from Theory to Practice
Jan 17, 2026
Master RNNs and learn how neural networks process sequential data with memory, from basic architecture to backpropagation through time.
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Understanding Long Short-Term Memory (LSTM) Networks: A Beginner's Guide with PyTorch
Jan 22, 2026
Master LSTM networks and solve the vanishing gradient problem with sophisticated memory management through gating mechanisms.
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Understanding Gated Recurrent Units (GRUs): A Beginner's Guide with PyTorch
Jan 25, 2026
Discover GRUs, the elegant simplification of LSTMs that achieves similar performance with fewer parameters and faster training.
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Understanding Bidirectional RNNs: A Beginner's Guide with PyTorch
Jan 28, 2026
Learn how bidirectional RNNs capture complete contextual information by processing sequences in both forward and backward directions.
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