Deep Learning with Python Tutorial

Welcome to this tutorial on deep learning with Python! In this guide, we will cover the basics of deep learning, including neural networks, backpropagation, and more. Whether you're a beginner or an experienced machine learning practitioner, this tutorial will provide you with the knowledge to build and train deep learning models.

Introduction to Deep Learning
Setting Up Your Environment
Neural Networks
Backpropagation
Building Your First Neural Network
Further Reading

Introduction to Deep Learning

Deep learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn and make intelligent decisions on its own.

Setting Up Your Environment

Before you start building and training neural networks, you need to set up your Python environment. We recommend using Anaconda, which is a Python distribution that includes all the necessary packages for deep learning.

conda create -n deep_learning_env python=3.7
conda activate deep_learning_env
conda install numpy scipy matplotlib

Neural Networks

A neural network is a series of algorithms that can recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

Backpropagation

Backpropagation is a key algorithm in neural networks that allows the network to learn and improve its performance by adjusting the weights of the connections between neurons.

Building Your First Neural Network

Now that you have a basic understanding of deep learning, it's time to build your first neural network. We will use the Keras library, which is a high-level neural networks API that runs on top of TensorFlow.

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(64, input_dim=100, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

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