So we’ve built our ML model — now what? How to get out of Jupyter Notebook and into Web Apps with Flask!

If you’ve been learning Deep Learning for a while now, you may have built some really cool models using your Jupyter notebook doing a range of things, from image recognition to language translation. You probably want to let your friends try out your models (or maybe even set up a company from this), but sending them a Jupyter notebook isn’t really what you had in mind. How do you build a Web Application to deploy your Machine Learning models?

The focus of this post is building a web app around our Machine Learning model for others to try. We will go through some Web programming techniques such as HTML and Flask, as well as deploying it on the Web on a Ubuntu server on DigitalOcean. …

How do we play Atari games just from pixel values? A gentle introduction to Deep Reinforcement Learning!

If you’ve read the news on Artificial Intelligence, you might have come across some of its impressive feats in playing games (from Atari games to DotA and Starcraft). These AI systems are often powered by reinforcement learning, and in this post, we’ll see how we can apply neural networks to reinforcement learning.

Series Note: This is the fifth and last post of the introductory series. I hope you’ve enjoyed this series — please and follow this publication to get more of this kind of content!

Where we left off: In part 1, we had an introduction to neural networks and how to make them work. We started off in Part 1a with a high-level summary of what Machine Learning aims to…

A step-by-step complete beginner’s guide to building your first Neural Network in a couple lines of code like a Deep Learning pro!

Writing your first Neural Network can be done with merely a couple lines of code! In this post, we will be exploring how to use a package called Keras to build our first neural network to predict if house prices are above or below median value. In particular, we will go through the full Deep Learning pipeline, from:

• Exploring and Processing the Data
• Building and Training our Neural Network
• Visualizing Loss and Accuracy
• Adding Regularization to our Neural Network

In just 20 to 30 minutes, you will have coded your own neural network just as a Deep Learning practitioner would have! …

A step-by-step guide to building your own image recognition software with Convolutional Neural Networks using Keras on CIFAR-10 images!

You can write your own image recognition software with just a few lines of code! In this post, we will see how to use Keras to build Convolutional Neural Networks to predict what’s inside a small image. We will go through the full Deep Learning pipeline, from:

• Exploring and Processing the Data
• Building and Training our Convolutional Neural Network
• Testing out with your own images

Pre-requisites:

This post assumes you’ve had extremely basic experience with using Keras to build a standard neural network. If you have not done so, please follow the instructions in the tutorial below:

• Build your first Neural Network to predict house prices with…

A step-by-step complete beginner’s guide to building your first Neural Network in a couple lines of code like a Deep Learning pro!

Writing your first Neural Network can be done with merely a couple lines of code! In this post, we will be exploring how to use a package called Keras to build our first neural network to predict if house prices are above or below median value. In particular, we will go through the full Deep Learning pipeline, from:

• Exploring and Processing the Data
• Building and Training our Neural Network
• Visualizing Loss and Accuracy
• Adding Regularization to our Neural Network

In just 20 to 30 minutes, you will have coded your own neural network just as a Deep Learning practitioner would have! …

A step-by-step guide to setting up Python for a complete beginner

You can code your own Data Science or Deep Learning project in just a couple of lines of code these days. This is not an exaggeration; many programmers out there have done the hard work of writing tons of code for us to use, so that all we need to do is plug-and-play rather than write code from scratch.

You may have seen some of this code on Data Science / Deep Learning blog posts. Perhaps you might have thought: “Well, if it’s really that easy, then why don’t I try it out myself?”

If you’re a beginner to Python and you want to embark on this journey, then this post will guide you through your first steps. A common complaint I hear from complete beginners is that it’s pretty difficult to set up Python. How do we get everything started in the first place so that we can plug-and-play Data Science or Deep Learning code? …

A step-by-step complete beginner’s guide to building your first Neural Network in a couple lines of code like a Deep Learning pro!

Writing your first Neural Network can be done with merely a couple lines of code! In this post, we will be exploring how to use a package called Keras to build our first neural network to predict if house prices are above or below median value. In particular, we will go through the full Deep Learning pipeline, from:

• Exploring and Processing the Data
• Building and Training our Neural Network
• Visualizing Loss and Accuracy
• Adding Regularization to our Neural Network

In just 20 to 30 minutes, you will have coded your own neural network just as a Deep Learning practitioner would have! …

What are Convolutional Neural Networks? How can we apply Neural Networks to recognize images?

From self-driving cars to facial recognition technology, our machines seem able to make sense of what they see. This is an impressive feat: after all, machines can only process images as a series of numbers. How does a machine translate this series of numbers into recognizing what object is in the image?

In this post, we will uncover the secrets of how Deep Learning has powered our cutting-edge image recognition technology.

This is Part 2 of an introductory series of Intuitive Deep Learning. Here’s a quick summary of what has happened thus far:

In Part 1, we had an introduction to neural networks and how to make them work. We started off in Part 1a with a high-level summary of what Machine Learning aims to…