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
- Installation
- Use
pip install tensorflow
for Python. - Or try the TensorFlow Playground for a browser-based demo.
- Use
- 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')
- Training a Model
- Use
model.fit()
to train with datasets. - Explore TensorFlow Tutorials for advanced topics.
- Use
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.