Explore machine learning's essentials for software engineers, delve into supervised learning, neural networks, and deep learning, and gain skills to tackle real-world data challenges effectively.

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

## Tune the learning rate

We’ll use our old hyperparameter called `lr`. This hyperparameter has been with us since almost the beginning of this course. Chances are, we already tuned it, maybe by trying a few random values. It’s time to be more precise about `lr` tuning.

To understand the trade-off of different learning rates, let’s go back to the basics and visualize gradient descent. The following diagrams show a few steps of GD along a one-dimensional loss curve, with three different values of `lr`. The red cross marks the starting point, and the green cross marks the minimum:

# Tune the learning rate

We’ll use our old hyperparameter called `lr`. This hyperparameter has been with us since almost the beginning of this course. Chances are, we already tuned it, maybe by trying a few random values. It’s time to be more precise about `lr` tuning.

To understand the trade-off of different learning rates, let’s go back to the basics and visualize gradient descent. The following diagrams show a few steps of GD along a one-dimensional loss curve, with three different values of `lr`. The red cross marks the starting point, and the green cross marks the minimum:

Learn what happens when we tweak the learning rate and batch size while training a neural network.

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

Tune Learning Rate and Batch Size

Tune the learning rate