🧠 AI Frameworks: Tools for Building Intelligent Systems

AI frameworks are essential for developers and researchers to create, train, and deploy machine learning models. Here's a guide to popular frameworks:

• TensorFlow 🤖
  Developed by Google, TensorFlow is an open-source library for numerical computation and large-scale machine learning.

  [Explore TensorFlow](https://cloud.ullrai.com/en/tech/ai/tensorflow)

• PyTorch 🧪
  Created by Facebook's AI Research lab, PyTorch is known for its dynamic computation graph and extensive ecosystem.

  [Learn more about PyTorch](https://cloud.ullrai.com/en/tech/ai/pytorch)

• Keras 📚
  A high-level API that simplifies neural network development, often used with TensorFlow as a backend.

  [Keras documentation](https://cloud.ullrai.com/en/tech/ai/keras)

• TensorFlow Lite 📱
  Optimized for mobile and embedded devices, enabling efficient deployment of ML models on IoT hardware.

  [TensorFlow Lite guide](https://cloud.ullrai.com/en/tech/ai/tensorflow-lite)

• MXNet 🌐
  A flexible deep learning framework supporting both imperative and symbolic programming.

  [MXNet community](https://cloud.ullrai.com/en/tech/ai/mxnet)

For a broader overview of AI technologies, visit:
AI Overview