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LLM_chatbot_Roadmap.md

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  1. Basic Neural Network Implementation

    • Objective: Understand the fundamentals of neural networks.
    • Project: Implement a simple feedforward neural network from scratch.
    • Skills Learned: Backpropagation, gradient descent, activation functions.
    • Technologies: Pure Python, NumPy.

  2. Using a Deep Learning Framework

    • Objective: Get familiar with a popular deep learning framework.
    • Project: Re-implement the basic neural network using PyTorch or TensorFlow.
    • Skills Learned: Using tensors, automatic differentiation, basic model training.
    • Technologies: PyTorch, TensorFlow.

  3. Text Data Preprocessing

    • Objective: Learn how to preprocess text data for NLP tasks.
    • Project: Implement text preprocessing techniques such as tokenization, stemming, lemmatization, and stop words removal.
    • Skills Learned: Text normalization, handling different text formats.
    • Technologies: NLTK, SpaCy.

  4. Word Embeddings and Text Representation

    • Objective: Understand how to represent text data in a way that neural networks can process.
    • Project: Implement and use word embeddings like Word2Vec or GloVe for text representation.
    • Skills Learned: Embedding layers, vector space models.
    • Technologies: Gensim, PyTorch, TensorFlow.

  5. Recurrent Neural Networks (RNNs) and LSTMs

    • Objective: Learn about RNNs for handling sequential data.
    • Project: Build an RNN or LSTM for a simple text generation task.
    • Skills Learned: Handling sequences, managing hidden states, text generation.
    • Technologies: PyTorch, TensorFlow.

  6. Transformer Models

    • Objective: Dive into the architecture behind modern LLMs.
    • Project: Implement a simple transformer model for a text-based task, such as translation or summarization.
    • Skills Learned: Attention mechanisms, positional encoding, multi-head attention.
    • Technologies: PyTorch, TensorFlow, Hugging Face Transformers.

  7. Fine-Tuning Pre-Trained Models

    • Objective: Leverage pre-trained models for specific NLP tasks.
    • Project: Fine-tune a pre-trained BERT or GPT model for a custom text classification task.
    • Skills Learned: Transfer learning, fine-tuning, handling large-scale pre-trained models.
    • Technologies: Hugging Face Transformers, PyTorch, TensorFlow.

  8. Building a Basic Chatbot

    • Objective: Create an interactive chatbot with basic conversational capabilities.
    • Project: Develop a simple rule-based chatbot to handle predefined interactions.
    • Skills Learned: Basic NLP techniques, intent recognition, response generation.
    • Technologies: NLTK, Rasa, Python.

  9. Developing a Context-Aware Chatbot

    • Objective: Enhance the chatbot with context management for more natural conversations.
    • Project: Implement a context-aware chatbot using an LSTM or Transformer-based model.
    • Skills Learned: Context tracking, managing stateful interactions.
    • Technologies: Rasa, PyTorch, TensorFlow.

  10. Integrating a Pre-Trained LLM into the Chatbot

    • Objective: Leverage a pre-trained LLM for sophisticated responses.
    • Project: Integrate GPT-3 (via OpenAI API) or another LLM into your chatbot for more advanced conversations.
    • Skills Learned: API integration, managing API limitations, ensuring coherent responses.
    • Technologies: OpenAI API, Hugging Face Transformers, Flask/Django for web integration.

  11. Polishing the User Interface

    • Objective: Make the chatbot user-friendly and visually appealing.
    • Project: Develop a web or mobile interface for your chatbot.
    • Skills Learned: Front-end development, integrating back-end AI models with the UI.
    • Technologies: React.js, Vue.js, HTML/CSS, Flask/Django, RESTful APIs.

  12. Deployment and Scaling

    • Objective: Deploy the chatbot and ensure it can handle multiple users.
    • Project: Deploy your chatbot on a cloud platform, ensure it is scalable and reliable.
    • Skills Learned: Cloud deployment, containerization (Docker), orchestration (Kubernetes).
    • Technologies: AWS/GCP/Azure, Docker, Kubernetes.