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Deep Learning

Learn Deep Learning as a connected topic across chapters, concepts, simulations, and interview reasoning.

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Learn Deep Learning as a connected topic across chapters, concepts, simulations, and interview reasoning.

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Machine Learning
Ai
Llm
Neural Networks

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Series that contain articles from Deep Learning. Select a path to filter the article list.

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Article 1Deep Learning Architectures: CNNs, RNNs, and TransformersTLDR: CNNs, RNNs, and Transformers solve different kinds of pattern problems. CNNs are great for spatial data like images, RNNs handle ordered sequences, and Transformers shine when long-range context13 minArticle 2Neural Networks Explained: From Neurons to Deep LearningTLDR: A neural network is a stack of simple "neurons" that turn raw inputs into predictions by learning the right weights and biases. Training means repeatedly nudging those numbers via back-propagati14 minArticle 3Softmax Function Explained: From Raw Scores to ProbabilitiesTLDR: Softmax converts a vector of raw scores (logits) into a valid probability distribution by exponentiating each value and dividing by the total. Subtracting the max before exponentiating prevents 23 minArticle 4Dot Product in Machine Learning: The Engine Behind Similarity, Attention, and Neural NetworksTLDR: The dot product multiplies corresponding elements of two vectors and sums the results. In machine learning it does three critical jobs: it scores semantic similarity between embeddings, computes22 minArticle 5Fine-Tuning LLMs with LoRA and QLoRA: A Practical Deep-DiveTLDR: LoRA freezes the base model and trains two tiny matrices per layer — 0.1 % of parameters, 70 % less GPU memory, near-identical quality. QLoRA adds 4-bit NF4 quantization of the frozen base, enab31 minArticle 6Transfer Learning Explained: Standing on the Shoulders of Pretrained ModelsTLDR: You don't need millions of labeled images or months of GPU time to build a great model. Transfer learning lets you borrow a pretrained network's hard-won feature detectors, plug in a new output 28 min

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