Welcome to our tutorial on Deep Learning with Python! This guide will walk you through the basics of deep learning, from setting up your environment to building and training your first neural network.

Prerequisites

Before you start, make sure you have the following prerequisites:

  • Basic understanding of Python programming
  • Familiarity with machine learning concepts
  • Access to a Python environment (e.g., Jupyter Notebook)

Setting Up Your Environment

To get started, you'll need to install the following packages:

  • TensorFlow: A powerful open-source library for machine learning and deep learning.
  • Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow.

You can install these packages using pip:

pip install tensorflow keras

Building Your First Neural Network

In this section, we'll build a simple neural network using Keras. This network will be able to classify images from the CIFAR-10 dataset.

Importing Libraries

First, import the necessary libraries:

import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten

Loading the Data

Next, load the CIFAR-10 dataset:

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

Preprocessing the Data

Before training the model, we need to preprocess the data:

x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

Building the Model

Now, let's build our neural network:

model = Sequential()
model.add(Flatten(input_shape=(32, 32, 3)))
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))

Compiling the Model

After building the model, we need to compile it:

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Training the Model

Now, we can train our model on the training data:

model.fit(x_train, y_train, epochs=10)

Evaluating the Model

Finally, evaluate the model on the test data:

test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print('\nTest accuracy:', test_acc)

Next Steps

Congratulations! You've just built and trained your first neural network using Python and Keras. To continue learning, we recommend exploring the following resources:

Stay curious and keep exploring the world of deep learning! 🌟