Introduction to TensorFlow 🧠🚀

TensorFlow is an open-source machine learning framework developed by Google, widely used for building and training neural networks. Below is a guide to get started with TensorFlow.

Core Concepts

• Tensors: N-dimensional arrays that flow through computational graphs.
• Graphs: Visual representations of operations and data flow.
• Sessions: Execute operations in a graph.
• Model Saving/Loading: Persist trained models for later use.

Getting Started

1. Installation
   • Use pip install tensorflow for Python.
   • Or try the TensorFlow Playground for a browser-based demo.
   
2. Basic Example

   import tensorflow as tf  
   model = tf.keras.Sequential([  
       tf.keras.layers.Dense(10, activation='relu', input_shape=(5,)),  
       tf.keras.layers.Dense(1)  
   ])  
   model.compile(optimizer='adam', loss='mean_squared_error')  
   

3. Training a Model
   • Use model.fit() to train with datasets.
   • Explore TensorFlow Tutorials for advanced topics.
   

Practical Use Cases

• 📊 MNIST Handwritten Digit Recognition
• 🖼️ Image Classification with CNNs
• 📖 Natural Language Processing (NLP)

Extend Your Learning

• Dive deeper into TensorFlow for Deep Learning
• Compare with other frameworks: PyTorch Tutorials | Keras Guides

Note: All images are placeholders for illustrative purposes.