TensorFlow Quickstart Guide 🚀

TensorFlow is an open-source machine learning framework developed by Google, widely used for building and training models in deep learning. Here's a quick overview to get started:

📋 Step 1: Install TensorFlow

• Python: Use pip install tensorflow for the latest version.
• Docker: Run docker run -it tensorflow/tensorflow:latest for a pre-configured environment.
• 📌 Check official installation guide for system requirements and advanced options.

🧠 Step 2: Understand Core Concepts

• Tensors: Multi-dimensional arrays that flow through the computational graph.
• Graphs: Visual representation of operations and data flow.
• Sessions: Execute operations in a TensorFlow graph.
• 📌 Explore TensorFlow basics with interactive examples.

📜 Step 3: Run a Simple Example

import tensorflow as tf  
# Define a simple computation graph  
x = tf.constant(5)  
y = tf.constant(10)  
result = tf.add(x, y)  
# Run the session  
with tf.Session() as sess:  
    print(sess.run(result))  # Output: 15  

• 🖼️ TensorFlow logo

🚀 Next Steps

• Try the MNIST handwritten digit classification tutorial for hands-on practice.

• Dive into TensorFlow models for pre-built solutions.

• 📌 Watch this video for a visual introduction.

• 🖼️ Code example | 🖼️ TensorFlow architecture

For more resources, visit our TensorFlow documentation hub. 🌐