Think Different

  Photo by Mikhail Nilov in pexel Fraud detection is a critical task in various industries, including finance, e-commerce, and healthcare. Generative AI can be used to identify patterns in data that indicate fraudulent activity. Tools and Libraries: Python: Programming language TensorFlow or PyTorch: Deep learning frameworks Scikit-learn: Machine learning library Pandas: Data manipulation library NumPy: Numerical computing library Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs): Generative AI models Code: Here's a high-level example of how you can use GANs for real-time fraud detection: Data Preprocessing: import pandas as pd from sklearn.preprocessing import StandardScaler # Load data data = pd.read_csv('fraud_data.csv') # Preprocess data scaler = StandardScaler() data_scaled = scaler.fit_transform(data) GAN Model: import tensorflow as tf from tensorflow.keras.layers import Input, Dense, Reshape, Flatten from tensorflow.keras.layers import BatchNo...

  Chatbot Application with Local LLM, RAG, LangChain, and Guardrail I've developed a chatbot application designed for informative and engaging conversationAs you already aware that Retrieval-augmented generation (RAG) is a technique that combines information retrieval with a set of carefully designed system prompts to provide more accurate, up-to-date, and contextually relevant responses from large language models (LLMs). By incorporating data from various sources such as relational databases, unstructured document repositories, internet data streams, and media news feeds, RAG can significantly improve the value of generative AI systems. Developers must consider a variety of factors when building a RAG pipeline: from LLM response benchmarking to selecting the right chunk size. In tapplication demopost, I demonstrate how to build a RAG pipeline uslocal LLM which can be converted to ing NVIDIA AI Endpoints for LangChain. FirI have you crdeate a vector storeconnecting with one of the ...

Photo by Lukas at pexel   To develop a pharmaceutical sales analytics system with geographical division and different categories of medicines, follow these steps: 1. Data Collection :    - Collect sales data from different regions.    - Gather data on different categories of medicines (e.g., prescription drugs, over-the-counter medicines, generic drugs).    - Include additional data sources like demographic data, economic indicators, and healthcare facility distribution. 2. Data Storage :    - Use a database (e.g., SQL, NoSQL) to store the data.    - Organize tables to handle regions, medicine categories, sales transactions, and any additional demographic or economic data. 3. Data Preprocessing :    - Clean the data to handle missing values and remove duplicates.    - Normalize data to ensure consistency across different data sources.    - Aggregate data to the required granularity (e.g., daily, weekly,...

  unplash You can convert a Colab notebook into a local Jupyter notebook by following these steps: Open the Colab notebook in a web browser. Click the File menu and select Download. In the Save As dialog box, select Jupyter Notebook (.ipynb) as the file type and click Save. The Jupyter notebook will be downloaded to your computer. Open the Jupyter notebook in a Jupyter notebook viewer. You can also use the following command to convert a Colab notebook into a local Jupyter notebook: jupyter nbconvert --to notebook <path- to -colab-notebook For example, to convert a Colab notebook named my_notebook.ipynb to a local Jupyter notebook, you would use the following command: jupyter nbconvert --to notebook my_notebook .ipynb Once you have converted a Colab notebook into a local Jupyter notebook, you can run it locally on your computer. I think your datasets are in Google Drive. It is easier to connet and work a google driver folder from Google Colab. However how will you do from your ...