kurtispykes / Deep-Learning

Curated articles and code on deep learning topics

Deep Learning

A collection of blog articles and code related to Deep Learning that I've curated.

I regularly write about Python, Machine Learning, Freelancing, Productivity, and Self-Branding in my Medium Blog. With a $5 a month commitment, you can unlock an unlimited access to stories on Medium. If you use my sign-up link, I'll recieve a small commision. If you're already a member, subscribe to recieve my posts directly to your inbox whenever I publish.

Downloading the code

The simplest way to download the code is to clone the repository with git clone:

git clone https://github.com/kurtispykes/Deep-Learning.git

The steps

1. git clone <repo>
2. cd <repo>
3. pip install virtualenv (if you don't already have virtualenv installed)
4. virtualenv venv to create your new environment (called 'venv' here)
5. venv/bin/activate.bat to enter the virtual environment
6. pip install -r requirements.txt to install the requirements in the current environment

Table of Contents

Neural Networks

• 7 Optimization methods used in Deep Learning
• Weight Initialization in Deep Neural Networks: See Full Code
• Improving the Accuracy of Your Neural Network: See Full Code
• Activation Functions in Neural Networks: See Full Code
• The Vanishing/Exploding Gradient Problem in Deep Neural Networks
• Varying Data To Build A Powerful Ensemble of Neural Netowrks: See Full Code
• Defining the Cost Function For Your Deep Neural Network

Deep Learning Algorithms From Scratch

• Artificial Neural Network: See Full Code

Regularization

• Dropout Regularization with Tensorflow Keras: See Full Code
• Combating Overfitting in Deep Learning