297 articles on neural networks, architectures, and training techniques — from backpropagation basics to diffusion models and KANs.
| Folder | Topics |
|---|---|
| 🏗️ Fundamentals-and-Architectures | Neurons, layers, gradient descent, network design |
| ⚡ Activation-Functions-and-Loss | ReLU, sigmoid, softmax, cross-entropy, custom losses |
| 🔄 Backpropagation-and-Optimization | Backprop, Adam, SGD, learning rate schedules |
| 🛡️ Regularization-and-Training-Tricks | Dropout, batch norm, data augmentation, mixed precision |
| 🖼️ CNN-and-Computer-Vision | ConvNets, ResNet, YOLO, image classification, object detection |
| 🔁 RNN-LSTM-and-Sequence | RNNs, LSTMs, GRUs, temporal modeling, seq2seq |
| 🎨 Autoencoders-and-Generative | VAEs, GANs, diffusion models, generative architectures |
| 🌀 KAN-and-Frameworks | Kolmogorov-Arnold Networks, PyTorch, TensorFlow, JAX |
- Backpropagation Algorithm in Neural Networks
- Batch Normalization in Neural Networks with Python
- Advanced Generative Adversarial Networks with Python
- Architecture of Recurrent Neural Networks in Python
- Accelerating Deep Learning with Mixed Precision Training
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