
Explore encoder, decoder, and sequence-to-sequence architectures and when to use them. Learn bidirectional encoders for classification, unidirectional decoders for generation, and seq2seq for translation and summarization.
Explore NLP task types, including sentence classification (sentiment analysis, spam detection) and sentence similarity. Learn word-level tagging (grammar components, ner) and text generation tasks like completion and summarization.
Learn how the transformer library from Hugging Face enables easy interaction with AI models via pre-trained models, cross-framework support with PyTorch and TensorFlow, and a simple pipeline-based sentiment analysis example.
Explore how to use the transformers pipeline class for sentiment analysis in Colab, loading Distilbert models and classifying English text as positive or negative.
Configure a llama model for text generation in VSCode by loading auto model and tokenizer. Authenticate with a token, specify the model name, and enable cuda if available for performance.
Build the chatbot user interface with the Gradio library, defining a chat interface and a response function with inputs for system message, max tokens, temperature, and top p.
Master NLP and Large Language Models (LLM): Build and deploy your ChatGPT-like chatbot with Python in record time.
Would you like to dive into artificial intelligence and create your own chatbot in just 2 hours? This is possible with our intensive course on NLP & LLM and Generative AI. We will teach you from scratch what a Large Language Model (LLM) is and how to leverage its power to develop innovative applications.
What You'll Learn:
Natural Language Processing (NLP) & Large Language Models (LLMs): Understand the architecture and inner workings of LLMs like GPT.
Transformers Library: Harness pipelines for sentiment analysis and entity recognition—key skills in natural language processing.
AutoClass Models: Get hands-on with AutoModel and AutoTokenizer to build question-answering systems.
Advanced Environments: Set up GPU configurations and create authentication tokens to work with sophisticated AI models.
Build a User Interface for the Chatbot: Create an intuitive chat-style interface to test your chatbot.
Open Source Models: Learn how to choose the right model based on the specific task at hand.
Chatbot Development: Build the chat logic, design an engaging user interface, and deploy your very own LLM-powered chatbot.
What You Need:
All you need is a basic knowledge of Python and a computer to start building your own chatbot.