pml_final.tar.gz

just_run

chapter06

SPA_07

SPA_gdb_frontEnd

J12-1

J12-2

SPA-server_image

Handson-10

Machine learning is the future for the next generation of software professionals.

This course serves as a guide to machine learning for software engineers. You’ll be introduced to three of the most relevant components of the AI/ML discipline; supervised learning, neural networks, and deep learning. You’ll grasp the differences between traditional programming and machine learning by hands-on development in supervised learning before building out complex distributed applications with neural networks. You’ll go even further by layering networks to create deep learning systems. You’ll work with complex real-world datasets to explore machine behavior from scratch at each phase.

By the end of this course, you’ll have a working knowledge of modern machine learning techniques. Using software engineering, you’ll be prepared to build complex neural networks and wrangle real-world data challenges.

Fundamentals of Machine Learning for Software Engineers

## Trace boundary with more than just a straight line
Let’s switch from the perceptron to the neural network. What does its decision boundary look like?

If we try the neural network on the linearly separable dataset, we’ll find that it gets the same perfect accuracy as the perceptron and a similarly straight decision boundary. However, on the non-linearly separable data, things get interesting.

Here’s the code that trains the neural network on the non linearly separable dataset:


# Trace boundary with more than just a straight line
Let’s switch from the perceptron to the neural network. What does its decision boundary look like?

If we try the neural network on the linearly separable dataset, we’ll find that it gets the same perfect accuracy as the perceptron and a similarly straight decision boundary. However, on the non-linearly separable data, things get interesting.

Here’s the code that trains the neural network on the non linearly separable dataset:


Learn about bendable decision boundaries.

How Machine Learning Works

Our First Learning Program

Walking the Gradient

Hyperspace

A Discern Machine

Get Real

The Final Challenge

The Perceptron

Designing the Network

Building the Network

Training the Network

How Classifiers Work

Batchin’ Up

The Zen of Testing

Let’s Do Development

A Deeper Kind of Network

Defeating Overfitting

Taming Deep Networks

Beyond Vanilla Networks

Into the Deep

Bend the Boundary

Trace boundary with more than just a straight line