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.
    tensor
  • Graphs: Visual representations of operations and data flow.
    computational_graph
  • Sessions: Execute operations in a graph.
  • Model Saving/Loading: Persist trained models for later use.

Getting Started

  1. Installation
    tensorflow_playground
  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
    tensorflow_tutorials

Practical Use Cases

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

Extend Your Learning

Note: All images are placeholders for illustrative purposes.