Deep-learning - TechnoScripts

deep-learning

Deep learning tutorials in Python covering neural networks, training, and common frameworks.

DL: RNNs and LSTMs for Sequence Processing

RNN LSTM models let you process sequential data that a plain feedforward network cannot, by reading one step at a time and remembering what came before. You will understand hidden states, the vanishing gradient problem in sequences, and how LSTM gates fix it, then build a simple RNN and a bidirectional sequence classifier in PyTorch, with every output run on a real machine. “LSTMs can learn to bridge time intervals in excess of 1000 steps. No other RNN architecture could ... Read More

NLP: Word Embeddings (Word2Vec, GloVe, FastText)

Word embeddings turn words into vectors that capture meaning. Train Word2Vec from scratch with Gensim, explore GloVe pre-trained embeddings, and see how FastText handles words it has never met before. “King minus Man plus Woman equals Queen” explained with real, tested code in this word2vec python guide. “You shall know a word by the company it keeps.” J.R. Firth, linguist Last Updated: July 2026 | Tested on: Gensim 4.4.0 on Python 3.13 (no 3.14 wheels yet) | Difficulty: Advanced | ... Read More

NLP: Transformer Architecture, Attention Is All You Need

This is the transformer architecture explained in plain language: the design that powers GPT, BERT, and every modern AI system. You will implement self-attention, multi-head attention, and positional encoding from scratch in Python. The Transformer is the single most important architecture in deep learning, and once it clicks you will see it everywhere. “Attention is all you need.” Ashish Vaswani, Google Brain Last Updated: July 2026 | Tested on: Python 3.14.6, PyTorch 2.12.1, NumPy 2.4.6 | Difficulty: Expert | Reading ... Read More