Deeplizard's Deep Learning Dictionary - Course Intro
Deeplizard's Deep Learning Dictionary
Welcome to deeplizard's Deep Learning Dictionary! This is a lightweight crash course containing bite-sized, practical and intuitive explanations for the most common terms and concepts in the field of deep learning.
Just as with any discipline, deep learning contains a host of its own vocabulary and lingo that may sometimes be hard to remember and manage in our minds, especially as beginners. We can use this Deep Learning Dictionary to receive concise high level overviews of these topics, which can be quickly and easily consumed for anyone's very first encounter with deep learning.
We can also use this course as a guide or cheat sheet to refer back to any time for quick refreshers of the foundational topics.
This series is beginner-friendly, so no prior deep learning or neural network knowledge is needed to understand the explanations provided here!
For absolute beginners to the field of deep learning and neural networks, we recommend first breezing through the Deep Learning Dictionary to casually make your first acquaintance with the fundamental topics, and then progress to the Deep Learning Fundamentals course for more elaborate explanations.
We cover many topics from the Deep Learning Dictionary in further depth with longer, more technical explanations in our Deep Learning Fundamentals course, which is excellent for beginners to gain a full, well-rounded understanding of all the foundational deep learning concepts needed to get started in this field.
What you'll learn
Using the Deep Learning Dictionary, we'll become acquainted with all the foundational topics needed to get started in the field of deep learning.
By taking this course, you will:
- Learn about the fields of AI, machine learning, and deep learning
- Gain an understanding for the various components that make up an artificial neural network
- Understand computer vision and how artificial neural networks achieve it
- Learn about the artificial neural network training process
- Understand how to prepare and pass data as input to a neural network
- Learn about network optimization, gradient descent, and backpropagation
- Understand the learnable parameters within an artificial neural network and how the network optimizes for them
- Learn about different network processes and their corresponding datasets: training, validation, testing
- Understand how to detect and approach problems that may arise during network training
- Learn techniques to improve and stabilize network training
- Understand the different categories of learning: supervised, unsupervised, semi-supervised
- Gain an understanding for all the components in a convolutional neural network (CNN)
- Learn how convolutional filters process image data
- Understand the general architecture of a CNN
- Learn how transfer learning works to transfer knowledge gained by one network to another
- Much more!
Check out the Deep Learning Dictionary course page for the full syllabus of all the topics we'll cover here.
This course includes:
92pages of fully written corresponding lecture notes
- Customized visual graphics created for this course
The course lessons will be released over time, but you may gain early access to the full course at once by registering on deeplizard.com! See instructions below for how to unlock early access to the entire course.
How To Register for Early Access
Registration consists of two steps: creating an account on deeplizard.com and purchasing early access to the course.
- Create a deeplizard account.
- Navigate to the Create page to create a new account.
- Enter a valid email address and password.
- Enter the verification code sent to your email address.
- Purchase early access to the course.
- Ensure you're logged in to deeplizard.com.
- Browse to the course page.
- Click the Enroll button.
- Agree to the course terms and conditions.
- Enter your payment details. If you have a discount code, you may enter it here.
- Upon successful payment, the course will become unlocked, and you may view the full contents. You will also receive a welcome email to begin the course.
We hope to see you in the course! Head over to preview the course by viewing the contents on the course page now!
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