Introduction to Neural Networks with TensorFlow

Course

Learn the fundamentals of neural networks with Python and TensorFlow, and then use your new skills to create your own image classifier—an application that will first train a deep learning model on a dataset of images and then use the trained model to classify new images.

Intermediate
3 weeks
Last Updated July 15, 2024

Skills you'll learn:

Training neural networksNumPy

Prerequisites:

Multivariable calculusBasic descriptive statisticsPython for data scienceBasic probabilityLinear algebra

Intermediate

3 weeks

Last Updated July 15, 2024

Skills you'll learn:

Training neural networks • NumPy • Backpropagation • Overfitting prevention

Prerequisites:

Multivariable calculus • Basic descriptive statistics • Python for data science

Course Lessons

Lesson 1

Course Introduction

Meet your instructors, get a short overview of what you'll be learning, check your prerequisites, and learn how to use the workspaces and notebooks found throughout the lessons.

Lesson 2

Introduction to Neural Networks

In this lesson, Luis will give you solid foundations on deep learning and neural networks. You'll also implement gradient descent and backpropagation in Python right here in the classroom.

Lesson 3

Implementing Gradient Descent

Mat will introduce you to a different error function and guide you through implementing gradient descent using numpy matrix multiplication.

Lesson 4

Training Neural Networks

Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.

Lesson 5

Deep Learning with TensorFlow

Learn how to use TensorFlow for building deep learning models.

Lesson 6 • Project

Image Classifier Project

In this project, you'll build a Python application that can train an image classifier on a dataset, then predict new images using the trained model.

Taught By The Best

Luis Serrano

Instructor

Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.

Mat Leonard

Content Developer

Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.

Juan Delgado

Content Developer

Juan is a computational physicist with a Masters in Astronomy. He is finishing his PhD in Biophysics. He previously worked at NASA developing space instruments and writing software to analyze large amounts of scientific data using machine learning techniques.

Michael Virgo

Instructor

After beginning his career in business, Michael utilized Udacity Nanodegree programs to build his technical skills, eventually becoming a Self-Driving Car Engineer at Udacity before switching roles to work on curriculum development for a variety of AI and Autonomous Systems programs.

The Udacity Difference

Combine technology training for employees with industry experts, mentors, and projects, for critical thinking that pushes innovation. Our proven upskilling system goes after success—relentlessly.

Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

Gain proven experience
Retain knowledge longer
Apply new skills immediately

Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.