Description
Course info
Rating
(129)
Level
Beginner
Updated
Feb 8, 2018
Duration
1h 27m
Description

Deep learning is a form of artificial intelligence that allows machines to learn how to solve complex tasks without being explicitly programmed to do so. In this course, Deep Learning: The Big Picture, you will first learn about the creation of deep neural networks with tools like TensorFlow and the Microsoft Cognitive Toolkit. Next, you'll touch on how they are trained, by example, using data. Finally, you will be provided with a high-level understanding of the key concepts, vocabulary, and technology of deep learning. By the end of this course, you'll understand what deep learning is, why it's important, and how it will impact you, your business, and our world.

About the author
About the author

Matthew is a data science consultant, author, and international public speaker. He has over 17 years of professional experience working with tech startups to Fortune 500 companies. He is a Microsoft MVP, ASPInsider, and open-source software contributor.

More from the author
Data Science: The Big Picture
Beginner
1h 9m
15 Sep 2017
Writing Testable Code
Beginner
2h 2m
5 May 2017
More courses by Matthew Renze
Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, I'm Matthew Renze with Pluralsight, and welcome to Deep Learning: The Big Picture. Deep Learning is a type of artificial intelligence and machine learning that has become extremely important in the past few years. Deep learning allows us to teach machines how to complete complex tasks without explicitly programming them to do so. As a result, people with the ability to teach machines using deep learning are in extremely high demand, and commanding significant increases in salary, as deep learning is revolutionizing the world around us. However, most developers and IT professionals have not yet learned this valuable set of skills. In this course, we'll answer the following three questions: what is deep learning, what can it do for you, and how do you get started with deep learning? To answer these questions, we'll learn about deep learning using deep neural networks, their applications, and potential impact on you, your organization, and our world. By the end of this course, you'll understand deep learning and the tools, technology, and trends driving the artificial intelligence revolution. As an introductory course, there are no required prerequisites for this course. We'll be explaining everything you need to know along the way. So please join us today at Pluralsight, and learn how to become part of the artificial intelligence revolution with Deep Learning: The Big Picture.

Introduction
Hello, and welcome to Deep Learning: The Big Picture. I'm Matthew Renze with Pluralsight, and in this course, we'll learn about Deep Learning from a high level perspective. Whether you realize it or not, our world is going through a major transition as we speak. We're entering the era of artificial intelligence and machine learning. A future where machines will be doing many of the jobs that humans are currently doing today. As a result, the software industry is also going through significant transition as well. In the past, we'd have to explicitly program a computer step by step how to solve a problem. This involved a lot of if then statements, for loops, and logical operations. In the future, machines will teach themselves how to solve problems, we just need to provide the data, and now, this is a radically different way of working with a computer than we're used to as programmers and IT professionals. While there's a growing demand with individuals with the skills necessary to implement deep learning, there's currently a shortage of people capable of teaching machines how to solve problems. As a result, those with the skills necessary to teach machines are commanding significantly higher salaries, and the trend doesn't seem to have any end in sight. The purpose of this course is to introduce you to this new way of teaching a computer how to solve a problem without explicitly programming it to do so. We'll do this by learning about deep learning, a powerful new form of artificial intelligence and machine learning that's revolutionizing the world around us.

Deep Learning
Hello again, and welcome to our next module on Deep Learning: The Big Picture. I'm Matthew Renze with Pluralsight, and in this module, we'll learn what deep learning is and how it works. As an overview of this module, first, we'll learn about artificial intelligence, which attempts to create machines that act rationally in response to their environment. Next, we'll learn about machine learning: software that learns by detecting statistical patterns in data. Then, we'll learn about artificial neurons, the building blocks of artificial neural networks. Next, we'll learn about neural networks: a type of machine learning based on a very crude approximation of neurons in a brain. Finally, we'll learn about deep neural networks: stacked layers of neural networks that can learn hierarchical representations of data. So let's get started.

Techniques
Hello again, and welcome back to Deep Learning: The Big Picture. I'm Matthew Renze with Pluralsight, and in this module, we'll learn about techniques that allow deep learning to solve a variety of problems. As an overview of this module, first, we'll learn about Fully Connected Feedforward Neural Networks: the standard network architecture used in most basic neural network applications. Next, we'll learn about Convolutional Neural Networks: a network architecture that works well for images, audio, and video. Then we'll learn about Recurrent Neural Networks: networks that work well for processing sequences of data over time. Next, we'll learn about Generative Adversarial Networks: a technique where we place two opposing neural networks in competition with one another in order to improve each other's performance. Finally, we'll learn about Reinforcement Learning: at technique for providing reward signals when multiple steps are necessary to achieve a goal. So let's get started.

Applications
Welcome back to Deep Learning: The Big Picture. I'm Matthew Renze with Pluralsight, and in this module, we'll learn about applications for deep learning. As an overview of this module, first, we'll learn about deep learning applications involving tabular data. Next, we'll learn about applications involving textual data. Then, we'll learn about applications for images; next, applications for audio, then applications for video, and finally, we'll learn about future applications for deep learning technologies. So let's get started.

Impact
Hello again, and welcome back to Deep Learning: The Big Picture. I'm Matthew Renze with Pluralsight, and in this module, and in this module, we'll learn about the impact of deep learning technologies. As an overview of this module, first, we'll learn about the history of deep learning in the obstacles this technology has overcome. Next, we'll learn about the trends that are currently enabling and driving the importance of deep learning. Finally, we'll look to the future to see what impact deep learning will likely have on a variety of industries over the next few years. So let's get started.

Next Steps
Hello again, and welcome to our final module on Deep Learning: The Big Picture. I'm Matthew Renze with Pluralsight, and in this module, we'll learn about next steps for getting started with deep learning. As an overview of this module, first, we'll learn about deep learning services: cloud-based APIs that you can call to solve common deep learning problems. Next, we'll learn about deep learning platforms: cloud-based solutions to solve specific deep learning problems. Then, we'll learn about deep learning frameworks: open source tools you can use to program your own deep learning solutions. Next, we'll learn about recommendations for which option to choose for each specific type of problem you're attempting to solve. Then, we'll learn where to go for more information on all of the topics in this course. Finally, we'll wrap things up for this module and for the course as a whole. So let's get started.