A complete guide to Artificial Neural Networks (ANN)-architecture, activation functions, forward and backpropagation, training, CNNs, RNNs, Transformers, Python & PyTorch code, and real-world applications.
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A definitive step-by-step guide from zero neural network knowledge to professional-grade deep learning engineer or researcher. Covers CNNs, RNNs, LSTMs, Transformers, LLMs, diffusion models, GANs, GNNs, multimodal AI, model compression, distributed training, and a realistic 14–20 month timeline. Written by an independent AI researcher.
Master the Perceptron with intuitive visuals, mathematical derivations, Python & PyTorch code, the XOR problem, and the foundations of modern neural networks.
Master Transformers in Machine Learning with this complete guide to Transformer architecture. Learn self-attention, multi-head attention, positional encoding, encoder-decoder models, GPT, BERT, Vision Transformers, mathematics, implementation, and real-world applications from first principles.
