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 You'll 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!
You may also like
- Top 10 Video Editing Courses to Learn in November 2024
- Top 10 Music Production Courses to Learn in November 2024
- Top 10 Animation Courses to Learn in November 2024
- Top 10 Digital Illustration Courses to Learn in November 2024
- Top 10 Renewable Energy Courses to Learn in November 2024
- Top 10 Sustainable Living Courses to Learn in November 2024
- Top 10 Ethical AI Courses to Learn in November 2024
- Top 10 Cybersecurity Fundamentals Courses to Learn in November 2024
- Top 10 Smart Home Technology Courses to Learn in November 2024
- Top 10 Holistic Health Courses to Learn in November 2024
- Top 10 Nutrition And Diet Planning Courses to Learn in November 2024
- Top 10 Yoga Instruction Courses to Learn in November 2024
- Top 10 Stress Management Courses to Learn in November 2024
- Top 10 Mindfulness Meditation Courses to Learn in November 2024
- Top 10 Life Coaching Courses to Learn in November 2024
- Top 10 Career Development Courses to Learn in November 2024
- Top 10 Relationship Building Courses to Learn in November 2024
- Top 10 Parenting Skills Courses to Learn in November 2024
- Top 10 Home Improvement Courses to Learn in November 2024
- Top 10 Gardening Courses to Learn in November 2024