Pinned Repositories
LangGraph_with_multiple_Tools_and_agents_by_semantic_route-and-Prompt_Engineer
This project integrates LangGraph with multiple tools and an agent, enhancing the language model's capabilities to efficiently perform complex tasks and generate coherent responses. It demonstrates the potential of combining tools with generative language models to improve the quality and relevance of automated responses.
QLoRA-with-Efficient-Finetuning-in-Large-Language-Models
"QLoRA with Text generation" This save more time and resource compared Full Fine-Tune! , developed using cutting-edge techniques "quantization" and Low Rank Adapters (LoRA)
Fine_Tune_tesxt-summarization-_by_T5_LLMs-NLP-
fine-tuning FLAN T5 LLMs for real-time text summarization in conversational chats using PyTorch. Our comprehensive tutorial empowers you to optimize these models for your specific needs, ensuring top-notch performance.
RAG-prompt-engineering-with-langchain-by-LLM
This project uses Retrieval-Augmented Generation (RAG) and prompt engineering with LangChain, powered by an LLM. Wikipedia documents are split into chunks with NLTK, embedded, and stored in a Chroma vector database. Retrieved documents are processed with a template prompt using LangChain's stuff chain to generate coherent responses.
Sign-Language-Alphabet-Recognition-by-mediapipe-and-ML-computer-vision
MediaPipe's Hand Landmarker detects hand landmarks in images. Use Python to follow these steps for Hand Landmarker. MediaPipe helps developers add computer vision and machine learning to apps without starting from scratch. It simplifies model creation and allows focus on the project's main parts
Sentiment-Analysis-Twitter-NLP-
Sentiment analysis: This project uses Hugging Face to analyze the sentiment of text. This can be used to understand how people feel about a product, service, or event.
Breast-Cancer-Classification-using-CNN-Model
A unique CNN architecture was meticulously crafted from scratch for this approach. The fusion of convolutional and pooling layers extracted intricate features from the breast cancer images. The cost of this approach was a longer training time, as the model developed features from scratch
-Breast-Cancer-Classification-using-Transfer-Learning
Breast cancer is a pressing health concern, highlighting early detection's significance. In this project, I tackled breast cancer image classification with Transfer Learning and CNNs. The aim was to compare these methods in terms of training time, accuracy, and convergence behavior.
-Scrape-web
Scrape 5 articles [link](https://coreyms.com/) and add them in excel sheet
AI-Jobes
AI
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