Welcome to the TensorFlow Basics tutorial! 🚀
TensorFlow is an open-source library for numerical computation and large-scale machine learning. Whether you're new to AI or just starting with TensorFlow, this guide will help you build a solid foundation.

🧠 Core Concepts

What is a Tensor?

A tensor is the central data structure in TensorFlow. Think of it as a multi-dimensional array.

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For example, a scalar (0D tensor), vector (1D), matrix (2D), and 3D arrays are all tensors. - **Scalar**: Single value (e.g., `5`) - **Vector**: 1D array (e.g., `[1, 2, 3]`) - **Matrix**: 2D array (e.g., `[[1, 2], [3, 4]]`) - **3D Tensor**: Multi-dimensional data (e.g., `[ [ [1, 2], [3, 4] ] ]`)

Sessions & Execution

In TensorFlow, you create a session to run operations.

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```python import tensorflow as tf sess = tf.Session() ``` Sessions allow you to execute computations on CPUs or GPUs. Learn more about [TensorFlow Sessions](/en/ai_tutorials/tensorflow_sessions) in our next tutorial!

Operations & Graphs

TensorFlow uses computational graphs to represent operations.

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- **Nodes**: Represent mathematical operations - **Edges**: Represent data (tensors) flowing between operations - **Graph**: A visual representation of the computation flow

📚 Practical Example: Hello World

Let's create a simple model to predict house prices. 

# Sample code
import tensorflow as tf

# Define variables
x = tf.constant(10)
y = tf.constant(20)

# Add operation
result = tf.add(x, y)

# Run session
with tf.Session() as sess:
    print("Result:", sess.run(result))

This example demonstrates basic tensor operations. For a deeper dive into TensorFlow Models, check our advanced guide!

🧪 Try It Yourself

Ready to experiment?

📖 Further Reading

Stay curious! 🌟
Image credits: TensorFlow logo and visualization examples are adapted from official resources.