A deep understanding of deep learning (with Python intro) — 90% OFF Coupon

Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.

★4.7 out of 5
51,452 students
Created by Mike X Cohen
English
Updated June 2026

Quick Facts — A deep understanding of deep learning (with Python intro) Overview

Here's a quick overview of everything you need to know about A deep understanding of deep learning (with Python intro) before you enroll:

•Course Name: A deep understanding of deep learning (with Python intro)
•Platform: Udemy
•Instructor: Mike X Cohen
•Coupon Last Verified: June 2, 2026
•Level: Advanced
•Topic: Development
•Subtopic: Deep Learning
•Total Time: 57h 30m of video content
•Language: English
•Access Type: Unlimited lifetime access + updates
•Certificate: Included upon completion from Udemy
•Main Skills: The theory and math underlying deep learning · Architectures of feedforward and convolutional networks · The calculus and code of gradient descent
•Requirements: Interest in learning about deep learning! · Python/Pytorch skills are taught in the course
•Current Price: $11.99 (was $119.99). You save $108.00 with 90% discount.
•How to Apply: Click the coupon button to activate your discount automatically
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Skills You'll Master in This Course

By the end of A deep understanding of deep learning (with Python intro), you'll have these practical skills:

✓The theory and math underlying deep learning .
✓Architectures of feedforward and convolutional networks .
✓The calculus and code of gradient descent .
✓Learn Python from scratch (no prior coding experience necessary) .
✓How to use transfer learning .
✓How to build artificial neural networks .
✓Building models in PyTorch .
✓Fine-tuning deep network models .
✓How and why autoencoders work .
✓Improving model performance using regularization .
✓Optimizing weight initializations .
✓Understand image convolution using predefined and learned kernels .
✓Whether deep learning models are understandable or mysterious black-boxes! .
✓Using GPUs for deep learning (much faster than CPUs!).

Prerequisites for This Course

Before enrolling in A deep understanding of deep learning (with Python intro), make sure you have:

•Interest in learning about deep learning!
•Python/Pytorch skills are taught in the course
•A Google account (google-colab is used as the Python IDE)

About This Udemy Course

The following is the full official course description for A deep understanding of deep learning (with Python intro) as published on Udemy by instructor Mike X Cohen:

Deep learning is increasingly dominating technology and has major implications for society.

From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology.

But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.

Deep learning is now used in most areas of technology, business, and entertainment. And it's becoming more important every year.

How does deep learning work?

Deep learning is built on a really simple principle: Take a super-simple algorithm (weighted sum and nonlinearity), and repeat it many many times until the result is an incredibly complex and sophisticated learned representation of the data.

Is it really that simple? mmm OK, it's actually a tiny bit more complicated than that ;)   but that's the core idea, and everything else -- literally everything else in deep learning -- is just clever ways of putting together these fundamental building blocks. That doesn't mean the deep neural networks are trivial to understand: there are important architectural differences between feedforward networks, convolutional networks, and recurrent networks.

Given the diversity of deep learning model designs, parameters, and applications, you can only learn deep learning -- I mean, really learn deep learning, not just have superficial knowledge from a youtube video -- by having an experienced teacher guide you through the math, implementations, and reasoning. And of course, you need to have lots of hands-on examples and practice problems to work through. Deep learning is basically just applied math, and, as everyone knows, math is not a spectator sport!

What is this course all about?

Simply put: The purpose of this course is to provide a deep-dive into deep learning. You will gain flexible, fundamental, and lasting expertise on deep learning. You will have a deep understanding of the fundamental concepts in deep learning, so that you will be able to learn new topics and trends that emerge in the future.

Please note: This is not a course for someone who wants a quick overview of deep learning with a few solved examples. Instead, this course is designed for people who really want to understand how and why deep learning works; when and how to select metaparameters like optimizers, normalizations, and learning rates; how to evaluate the performance of deep neural network models; and how to modify and adapt existing models to solve new problems.

You can learn everything about deep learning in this course.

In this course, you will learn
  • Theory: Why are deep learning models built the way they are?
  • Math: What are the formulas and mechanisms of deep learning?
  • Implementation: How are deep learning models actually constructed in Python (using the PyTorch library)?
  • Intuition: Why is this or that metaparameter the right choice? How to interpret the effects of regularization? etc.
  • Python: If you're completely new to Python, go through the 8+ hour coding tutorial appendix. If you're already a knowledgeable coder, then you'll still learn some new tricks and code optimizations.
  • Google-colab: Colab is an amazing online tool for running Python code, simulations, and heavy computations using Google's cloud services. No need to install anything on your computer.

Unique aspects of this course
  • Clear and comprehensible explanations of concepts in deep learning, including transfer learning, generative modeling, convolutional neural networks, feedforward networks, generative adversarial networks (GAN), and more.
  • Several distinct explanations of the same ideas, which is a proven technique for learning.
  • Visualizations using graphs, numbers, and spaces that provide intuition of artificial neural networks.
  • LOTS of exercises, projects, code-challenges, suggestions for exploring the code. You learn best by doing it yourself!
  • Active Q&A forum where you can ask questions, get feedback, and contribute to the community.
  • 8+ hour Python tutorial. That means you don't need to master Python before enrolling in this course.

So what are you waiting for??

Watch the course introductory video and free sample videos to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up.

I hope to see you soon in the course!

Mike

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Is the A deep understanding of deep learning (with Python intro) Coupon Worth It?

Expert review by Andrew Derek, Lead Course Analyst at CoursesWyn.•Last updated: June 2, 2026.

Based on analysis of the curriculum structure, student engagement metrics, and verified rating data, A deep understanding of deep learning (with Python intro) is a high-value resource for learners seeking to build skills inDevelopment. Taught by Mike X Cohen on Udemy, the 57h 30m course provides a structured progression from foundational concepts to advanced techniques— making it suitable for learners at all levels. The current coupon reduces the price by 90%, from $119.99 to $11.99, removing the primary financial barrier to enrollment.

✓What We Like (Pros)

  • Verified 90% price reduction makes this course accessible to learners on any budget.
  • Aggregate student rating of 4.7 out of 5 indicates high learner satisfaction.
  • Strong enrollment base with over 51,452 students demonstrates course popularity and trust.
  • Includes an official Udemy completion certificate and lifetime access to all future content updates.

!Keep in Mind (Cons)

The following limitations should be considered before enrolling in A deep understanding of deep learning (with Python intro):

  • The depth of Development coverage may be challenging for absolute beginners without the listed prerequisites.
  • Lifetime access is contingent on the continued operation of the Udemy platform.
  • Hands-on projects and quizzes require additional time investment beyond video watch time.
Final Verdict: Worth It
This course offers exceptional value with current pricing
✓

Course Rating Summary

A deep understanding of deep learning (with Python intro) has earned an aggregate rating of 4.7 out of 5 from 51,452 verified student reviews on Udemy. Below is the detailed rating distribution showing learner satisfaction across all star levels.

4.7
★★★★★
51,452 Verified Ratings
5 stars
75%
4 stars
15%
3 stars
6%
2 stars
2%
1 star
2%

* Rating distribution is approximated from the aggregate score. Sourced from Udemy.

About the Instructor — Mike X Cohen

A deep understanding of deep learning (with Python intro) is taught by Mike X Cohen, a Udemy instructor specializing in Development. For the full instructor biography, professional credentials, and a complete list of their courses, visit the official instructor profile on Udemy.

•Instructor Name: Mike X Cohen
•Subject Area: Development
•Teaching Approach: Practical, project-based instruction focused on real-world application of Development skills.

Frequently Asked Questions

The following questions and answers cover the most common queries about A deep understanding of deep learning (with Python intro), its coupon code, pricing, and enrollment process.

About the Author

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Andrew Derek

Lead Course Analyst at CoursesWyn with 8+ years of experience evaluating online learning platforms. I've analyzed 500+ Udemy courses and helped thousands of learners choose the right courses for their career goals.

⭐4.8/5 Rating
✓Trusted by 10K+ Students

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