The Deep Learning Masterclass Convert Sketch to Photo

The Deep Learning Masterclass – Convert Sketch to Photo, available at $19.99, has an average rating of 4.38, with 50 lectures, based on 4 reviews, and has 77 subscribers.

You will learn about Build machine learning models Apply for high-paid jobs or work as a freelancer in one the most-demanded sectors Provide amazing user experiences Build powerful, fast, user-friendly and reactive machine learning experience This course is ideal for individuals who are Developers transferring from other languages or Anyone who wants to learn how and why of Machine Learning or Anyone who wants to learn how and why of Deep Learning or Anyone who wants to learn how and why of Python It is particularly useful for Developers transferring from other languages or Anyone who wants to learn how and why of Machine Learning or Anyone who wants to learn how and why of Deep Learning or Anyone who wants to learn how and why of Python.

Enroll now: The Deep Learning Masterclass – Convert Sketch to Photo

Summary

Title: The Deep Learning Masterclass – Convert Sketch to Photo

Price: $19.99

Average Rating: 4.38

Number of Lectures: 50

Number of Published Lectures: 50

Number of Curriculum Items: 50

Number of Published Curriculum Objects: 50

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Build machine learning models

Apply for high-paid jobs or work as a freelancer in one the most-demanded sectors

Provide amazing user experiences

Build powerful, fast, user-friendly and reactive machine learning experience

Who Should Attend

Developers transferring from other languages

Anyone who wants to learn how and why of Machine Learning

Anyone who wants to learn how and why of Deep Learning

Anyone who wants to learn how and why of Python

Target Audiences

Developers transferring from other languages

Anyone who wants to learn how and why of Machine Learning

Anyone who wants to learn how and why of Deep Learning

Anyone who wants to learn how and why of Python

Deep learning is not like any other technology, but it is in many cases the only technology that can solve certain problems. We need to ensure that all people involved in the project have a common understanding of what is required, how the process works, and that we have a realistic view of what is possible with the tools at hand. To boil down all this to its core components we could consider a few important rules:

create a common ground of understanding, this will ensure the right mindset

state early how progress should be measured

communicate clearly how different machine learning concepts works

acknowledge and consider the inherited uncertainty, it is part of the process

In order to define AI, we must first define the concept of intelligence in general. A paraphrased definition based on Wikipedia is:

Intelligence can be generally described as the ability to perceive information and retain it as knowledge to be applied towards adaptive behaviors within an environment or context.

While there are many different definitions of intelligence, they all essentially involve learning, understanding, and the application of the knowledge learned to achieve one or more goals.

Is this course for me?

By taking this course, you will gain the tools you need to continue improving yourself in the field of app development. You will be able to apply what you learned to further experience in making your own apps able to perform more.

No experience necessary. ?Even if you’ve never coded before, you can take this course. ??One of the best features is that you can watch the tutorials at any speed you want. This means you can speed up or slow down the video if you want to!

When your learning to code, you often find yourself following along with a tutor without really knowing why you’re doing certain things. In this course, I will demonstrate correct coding as well as mistakes I often see and how to avoid them.

Course Curriculum

Chapter 1: Course Overview

Lecture 1: 01 Project Preview

Lecture 2: 02 What Youll Need

Chapter 2: Mammoth Interactive Course Intro

Lecture 1: 00 About Mammoth Interactive

Lecture 2: 01 How To Learn Online Effectively

Chapter 3: Introduction to Python (Prerequisite)

Lecture 1: 00. Intro To Course And Python

Lecture 2: 01. Variables

Lecture 3: 02. Type Conversion Examples

Lecture 4: 03. Operators

Lecture 5: 04. Collections

Lecture 6: 05. List Examples

Lecture 7: 06. Tuples Examples

Lecture 8: 07. Dictionaries Examples

Lecture 9: 08. Ranges Examples

Lecture 10: 09. Conditionals

Lecture 11: 10. If Statement Examples

Lecture 12: 11. Loops

Lecture 13: 12. Functions

Lecture 14: 13. Parameters And Return Values Examples

Lecture 15: 14. Classes And Objects

Lecture 16: 15. Inheritance Examples

Lecture 17: 16. Static Members Examples

Lecture 18: 17. Summary And Outro

Chapter 4: Machine Learning Fundamentals

Lecture 1: 01 What Is Machine Learning

Lecture 2: 02 What Is Deep Learning

Lecture 3: 03 What Is A Neural Network

Lecture 4: 04 What Is Unsupervised Learning

Lecture 5: Source Files

Chapter 5: Data Processing

Lecture 1: 01 Load Dataset

Lecture 2: 02 Process Photos And Sketches

Lecture 3: Source Files

Chapter 6: Generative Neural Network Fundamentals

Lecture 1: 01 What Is A Generative Neural Network

Lecture 2: 02 What Is A Convolutional Neural Network

Lecture 3: 03 How To Build A Convolutional Neural Network

Lecture 4: 04 How Do You Build A Generator

Lecture 5: Source Files

Chapter 7: Build Neural Networks to Convert a Sketch to a Photograph

Lecture 1: 01 Build A Generator

Lecture 2: 02 Build A Discriminator

Lecture 3: 03 Build A Combined Model

Lecture 4: Source Files

Chapter 8: Discriminator Neural Network Fundamentals

Lecture 1: 01 How Do You Build A Discriminator

Lecture 2: Source Files

Chapter 9: Train the Model

Lecture 1: 01 Performance Of A Machine Learning Algorithm

Lecture 2: 02 What Is Error

Lecture 3: 03 What Is The Adam Optimizer

Lecture 4: 04 Define Loss And Optimizers

Lecture 5: 05 Build A Training Epoch

Lecture 6: Source Files

Chapter 10: Test the Model

Lecture 1: 01 Test The Model

Lecture 2: 02 How To Improve The Model

Lecture 3: Source Files

Instructors

Mammoth Interactive
Top-Rated Instructor, 3.3 Million+ Students

John Bura
Best Selling Instructor Web/App/Game Developer 1Mil Students

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 1 votes

4 stars: 1 votes

5 stars: 2 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!