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Deep learning in Python is typically implemented using frameworks like TensorFlow, Keras, or PyTorch, which provide high-level APIs for building and training neural networks efficiently. Below is a simple example of an image classification model using Keras with TensorFlow backend.
Steps to Build a Basic Deep Learning Model
1. Import Required Libraries
import tensorflow as tffrom tensorflow import kerasfrom tensorflow.keras import layersCopied!âś•Copy2. Load and Prepare Dataset Here we use the MNIST dataset for handwritten digit recognition.
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()x_train = x_train.astype("float32") / 255.0x_test = x_test.astype("float32") / 255.0Copied!âś•Copy3. Define the Model Architecture
model = keras.Sequential([layers.Flatten(input_shape=(28, 28)),layers.Dense(128, activation='relu'),layers.Dropout(0.2),layers.Dense(10, activation='softmax')])Copied!âś•Copy4. Compile the Model
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])Copied!âś•Copy Deep Learning with Python - Read Online
Read the third edition of Deep Learning with Python online, for free. Build from the basics to state-of-the-art techniques with Python code you can run from your …
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Deep Learning with Python Guide - PyTutorial
Jan 7, 2026 · Learn the fundamentals of deep learning with Python, covering neural networks, key libraries like TensorFlow, and building your first AI model step by step.
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