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This course covers building prompt systems with OpenAI GPT-3, few-shot training, adding external info and final thoughts on generative AI and prompts.
This course covers 4 components of LangChain and uses Hugging Face and OpenAI LLMs to build and analyze models. Students get to experience GPT-3 and see the final results.
Learn to build a generative AI for question-answering using Python in less than an hour with James Briggs. Understand AI, data preprocessing, text embedding, and get results.
Learn to use Falcon 40B, the top-performing open-source Large Language Model, with Hugging Face Transformers and LangChain to create chatbots. Offered by James Briggs.
Learn to interact with the web using Python, covering essentials like APIs, JSON, and Python requests. Offered by James Briggs, it takes less than an hour.
for chatbots. Learn about implementing memory for better conversational AI, explore LangChain's Memory & see examples.
Learn to build a multi-modal hybrid search engine for eCommerce using OpenAI's CLIP, BM25, and Python in under an hour with James Briggs.
Learn to build robust vector similarity search applications with James Briggs. Understand composite indexes, vector transformations, and balance between recall, latency, and memory usage.
Explore the vast repository of language-focused datasets and learn to build efficient pre-processing pipelines with HuggingFace Datasets in under an hour.
Learn to build a Bert WordPiece Tokenizer in Python and HuggingFace with James Briggs. This less than 1-hour material covers transformer models, tokenization, and coding walkthroughs.
Learn to compress high-dimensional vectors using Product Quantization, reducing memory usage by 97% and increasing search speeds. Offered by James Briggs, it takes less than an hour.
Learn to efficiently search large datasets using Facebook AI Similarity Search (Faiss) in Python with James Briggs. Understand the use and impact of different indexes in under an hour.
Learn to use Facebook AI Similarity Search (FAISS) for efficient similarity search with James Briggs. Less than 1-hour material covering index training, adding vectors, and product quantization.
Learn to train BERT with BertForPretraining in less than an hour with James Briggs. The training includes understanding NSP and MLM logic.
Learn to fine-tune BERT models using Masked-Language Modeling (MLM) in PyTorch with James Briggs. Ideal for domain-specific language tasks. Less than 1-hour workload.
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