ai-tutorials/tensorflow-supervised

TensorFlow Supervised Learning Tutorials

Supervised learning is a fundamental concept in machine learning, where we train models on labeled data. TensorFlow, being one of the most popular machine learning frameworks, provides extensive support for supervised learning tasks. Below are some tutorials that can help you get started with TensorFlow Supervised Learning.

Tutorials

Basic Concepts: Learn the basics of supervised learning and TensorFlow.
- TensorFlow Supervised Learning: A Beginner's Guide
Linear Regression: Implement linear regression using TensorFlow.
- TensorFlow Linear Regression Tutorial
Neural Networks: Dive into neural networks and their implementation in TensorFlow.
- TensorFlow Neural Networks Tutorial
Convolutional Neural Networks (CNNs): Understand and implement CNNs for image classification.
- TensorFlow CNN Tutorial
Recurrent Neural Networks (RNNs): Learn about RNNs and their applications in time series analysis.
- TensorFlow RNN Tutorial

Resources

TensorFlow Official Documentation - For comprehensive documentation on TensorFlow.

Example: Neural Networks

Here's a snippet of code to get you started with neural networks in TensorFlow:

import tensorflow as tf

model = tf.keras.Sequential([
  tf.keras.layers.Dense(10, activation='relu', input_shape=(32,)),
  tf.keras.layers.Dense(1)
])

model.compile(optimizer='adam',
              loss='mean_squared_error')

For more detailed examples and explanations, check out the TensorFlow Neural Networks Tutorial.