Deep learning, a subset of machine learning, has become essential in data science, powering advancements in image recognition, NLP, and predictive analytics. As data scientists, using the right tools is critical for building, training, and deploying deep learning models efficiently.
Frameworks simplify complex tasks, boost productivity, and provide flexibility for customization. They enable data scientists to focus on refining models instead of reinventing the wheel.
TensorFlow, developed by Google, is known for its flexibility and scalability. It can handle various types of data science tasks, from research to large-scale production.
import tensorflow as tf
from tensorflow.keras import datasets, layers, models# Load data
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
# Build CNN model
model = models.Sequential([
layers.Conv2D(32, (3, 3)…