Welcome to the Deep Learning with Python tutorial! This guide will help you get started with deep learning using Python. We will cover the basics, including installing necessary libraries and building your first neural network.

Prerequisites

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

  • Python 3.6 or higher
  • Jupyter Notebook (optional, for interactive learning)
  • Anaconda (optional, for managing environments)

Install Libraries

To get started, you'll need to install some libraries. We recommend using Anaconda to manage your environments. Here's how to install the necessary libraries:

conda create -n dl_env python=3.8
conda activate dl_env
pip install numpy pandas matplotlib scikit-learn tensorflow

Your First Neural Network

Now, let's build your first neural network. We will use the TensorFlow library for this example.

import tensorflow as tf

# Create a simple model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(100,)),
    tf.keras.layers.Dense(1)
])

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

# Generate some data
import numpy as np

x_train = np.random.random((1000, 100))
y_train = np.random.random((1000, 1))

# Train the model
model.fit(x_train, y_train, epochs=10)

This code creates a simple neural network with one input layer, one hidden layer with 10 neurons, and one output layer. We then compile the model using the Adam optimizer and mean squared error loss function. We generate some random data to train the model for 10 epochs.

More Resources

For further learning, we recommend the following resources:

TensorFlow Logo

Enjoy your journey into the world of deep learning with Python!