With lots of examples and practice problems, you will master deep learning using PyTorch in an experimental scientific manner.
The technology of deep learning is rapidly transforming and has significant implications for society.
A new generation of deep learning algorithms is transforming every aspect of modern technology, from self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to the creation of music.
In addition to high-end, cutting-edge applications, deep learning is not just about glitz and glam. As machine learning, data science, and statistics become increasingly automated, deep learning is fast becoming the standard tool. Researchers and governments use deep learning to detect patterns in their research data, while small businesses use it to perform data mining and dimension reduction.
The use of deep learning is now being introduced into most sectors of technology and business. Moreover, it is becoming more and more important every year.
How does deep learning work?
The principle of deep learning is rather simple: Take a really simple algorithm (weighted sum and nonlinearity), and repeat it many, many times until the result is an incredibly complex and sophisticated representation of the data.
How simple can it be? Actually, it's more complicated than that 😉 but that's the core idea, and everything else in deep learning is just clever ways to combine these fundamental building blocks. However, this doesn't mean understanding deep neural networks is trivial: there are significant architectural differences between feedforward networks, convolutional networks, and recurrent networks.
Due to the diversity of deep learning model designs, parameters, and applications, you can only learn deep learning effectively — not just superficially, from a youtube (aff) video (aff) — by having an experienced teacher guide you through the math, implementations, and reasoning. Furthermore, you need plenty of hands-on examples and practice problems to work through. Learning deep things is fundamentally just applying math, and as everyone knows, math is not a spectator sport.
What is this course all about?
In a nutshell, this course is designed to provide in-depth knowledge on deep learning. As a result, you will gain deep learning expertise that is fundamental, flexible, and lasting. This A deep understanding of deep learning (with Python intro) course will give you a solid understanding of the fundamentals of deep learning, so that you can keep up with the newest topics and trends.
There are some solved examples, but this is not a class for someone who wants a quick overview of deep learning. It is instead designed for people who 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 deep understanding of deep learning (with Python intro) 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 deep understanding of deep learning (with Python intro) course
- Clear and comprehensible explanations of concepts in deep learning.
- 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 A deep understanding of deep learning (with Python intro) course.
So what are you waiting for??
Learn more about this course and my teaching style by watching A deep understanding of deep learning (with Python intro) course introductory video (aff) and free sample videos (aff). I would be happy to answer your questions if you are not sure if this course is right for you and want to learn more before you enroll.
I hope to see you soon in the course!
Who this course is for:
- Students in a deep learning course
- Machine-learning enthusiasts
- Anyone interested in mechanisms of AI (artificial intelligence)
- Data scientists who want to expend their library of skills
- Aspiring data scientists
- Scientists and researchers interested in deep learning
Created by Mike X Cohen
Last updated 8/2021
Size: 20.8 GB