Welcome to the TensorFlow tutorial! This guide will walk you through creating a simple neural network to classify handwritten digits using the MNIST dataset. Let's get started!
📚 What is TensorFlow?
TensorFlow is an open-source machine learning framework developed by Google. It allows developers to build and train models efficiently, whether you're working on deep learning, natural language processing, or computer vision projects.
🧰 Getting Started with TensorFlow
1. Installation
Install TensorFlow using pip:
pip install tensorflow
For GPU support, ensure you have CUDA and cuDNN installed.
2. Importing Libraries
Start by importing TensorFlow and other necessary libraries:
import tensorflow as tf
from tensorflow.keras import layers, models
3. Building a Model
Here's a simple example using the MNIST dataset:
model = models.Sequential([
layers.Flatten(input_shape=(28, 28)),
layers.Dense(128, activation='relu'),
layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
📈 Training and Evaluating
Load the dataset and train the model:
mnist = tf.keras.datasets.mnist.load_data()
(x_train, y_train), (x_test, y_test) = mnist
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test, verbose=2)
🧠 Extend Your Knowledge
Explore more TensorFlow resources:
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Happy coding! 🧪 Let us know if you need further assistance.