Welcome to the TensorFlow basics tutorial! Whether you're new to machine learning or just starting with TensorFlow, this guide will help you get familiar with the core concepts and tools. Let's dive in!

What is TensorFlow? 🤖

TensorFlow is an open-source machine learning framework developed by Google. It allows developers to create machine learning models and perform numerical computations using data flow graphs.

tensorflow logo

Getting Started 🚀

Installation

To begin, install TensorFlow using pip: 

pip install tensorflow

For more details on installation, check our TensorFlow Getting Started guide.

install tensorflow

Key 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 environment.

tensor example

Simple Code Example 💻

Here's a basic TensorFlow code snippet to get you started: 

import tensorflow as tf

# Define a simple computation graph
a = tf.constant(5)
b = tf.constant(10)
c = tf.add(a, b)

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

Try running this code and see the output!

training model

Building Your First Model 🏗️

  1. Import TensorFlow: Start by importing the library.
  2. Define Model Structure: Use layers and functions to build your model.
  3. Compile and Train: Configure the model and train it on data.

model building

Next Steps 🌟

summary tensorflow

Happy coding! 🚀