Welcome to the TensorFlow tutorial! TensorFlow is an open-source machine learning framework developed by Google, widely used for tasks like data flow graphs, neural networks, and large-scale distributed computing. Let's dive into the essentials.

🧠 What is TensorFlow?

TensorFlow allows developers to create complex machine learning models and algorithms. Its core concept is tensors—multi-dimensional arrays that represent data. Here's a quick overview:

  • Core Features:
    • Flexible architecture for research and production
    • Support for both CPU and GPU computing
    • Extensive libraries for deep learning (e.g., Keras, TF.js)
  • Use Cases:
    • Image and speech recognition
    • Natural Language Processing (NLP)
    • Reinforcement learning

📦 Getting Started

To begin with TensorFlow, you'll need to install it first. Use the following command:

pip install tensorflow

Once installed, you can start by importing TensorFlow in your Python script:

import tensorflow as tf

🧾 Example Code

Here's a simple example to get you started with TensorFlow:

# Define a simple model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(None, 5)),
    tf.keras.layers.Dense(1)
])

# Compile the model
model.compile(optimizer='adam', loss='mse')

# Generate some dummy data
import numpy as np
data = np.random.rand(1000, 5)
labels = np.random.rand(1000, 1)

# Train the model
model.fit(data, labels, epochs=10)

📚 Further Reading

For more advanced topics, check out our other tutorials:

TensorFlow
Neural Network