I am working on a series of Natural Language Processing prototypes and need a knowledgeable partner who can walk me through the practical use of recurrent models (RNN/LSTM/GRU) and modern Transformer-based architectures. My main goal is to move beyond theory and confidently apply these networks to real text problems, from data preparation all the way to evaluation and error analysis. Here is what will make the collaboration valuable to me: • Live, example-driven explanations of how and why to pick an RNN versus a Transformer for a given text task. • Step-by-step coding sessions (Python, PyTorch or TensorFlow—whichever you prefer) that start with a raw dataset and finish with a trained, evaluated model. • Clear guidance on fine-tuning popular checkpoints such as BERT, RoBERTa or GPT-style models, including tips on hyper-parameters, regularisation and efficient training on limited hardware. • Recommendations on best-practice preprocessing (tokenisation, embeddings, padding, batching) and how those differ between recurrent and attention-based networks. • Hands-on walkthroughs of evaluation metrics—accuracy, F1, BLEU, any that fit the chosen use case—and how to interpret them in a production setting. • Short written notes or Jupyter notebooks after each session so I can revisit the material later. Acceptance criteria 1. By the end of the engagement I can independently set up, train and evaluate at least one RNN and one Transformer model on a text dataset of my choice. 2. All demonstration code runs end-to-end on my machine and is clearly commented. 3. Explanations are delivered in plain language, with any math kept to what is strictly necessary for understanding the implementation. If you enjoy demystifying deep-learning concepts and can back theory with clean, runnable code, I’d love to get started.