Practical Deep Learning with PyTorch
What you'll learn
- Effectively wield PyTorch, a Python-first framework, to build your deep learning projects
- Master deep learning concepts and implement them in PyTorch
Requirements
- You need to know basic python such as lists, dictionaries, loops, functions and classes
- You need to know basic differentiation
- You need to know basic algebra
Description
Growing Importance of Deep Learning
Deep learning underpins a lot of important and increasingly important applications today ranging from facial recognition, to self-driving cars, to medical diagnostics and more.
Made for Anyone
Although many courses are very mathematical or too practical in nature, this course strikes a careful balance between the two to provide a solid foundation in deep learning for you to explore further if you are interested in research in the field of deep learning and/or applied deep learning. It is purposefully made for anyone without a strong background in mathematics. And for those with a strong background, it would accelerate your learning in understanding the different models in deep learning.
Code As You Learn
This entire course is delivered in a Python Notebook such that you can follow along the videos and replicate the results. You can practice and tweak the models until you truly understand every line of code as we go along. I highly recommend you to type every line of code when you are listening to the videos as this will help a lot in getting used to the syntax.
Gradual Learning Style
The thing about many guides out there is that they lack the transition from the very basics and people often get lost or miss out vital links that are critical in understanding certain models. Because of this, you can see how every single topic is closely linked with one another. In fact, at the beginning of every topic from logistic regression, I take the time to carefully explain how one model is simply a modification from the previous. That is the marvel of deep learning, we can trace back some part of it to linear regression where we will start.
Diagram-Driven Code
This course uses more than 100 custom-made diagrams where I took hundreds of hours to carefully create such that you can clearly see the transition from one model to another and understand the models comprehensively. Also, the diagrams are created so you can clearly see the link between the theory that I would teach and the code you would learn.
Mentor Availability
When I first started learning, I wished I had a mentor to guide me through the basics till the advanced theories where you can publish research papers and/or implement very complicated projects. And this course provides you with free access to ask any question, no matter how basic. I will be there and try my very best to answer your question. Even if the material is covered here, I will take the effort to point you to where you can learn here and more resources beyond this course.
Math Prerequisite FAQ
This is not a course that emphasizes heavily on the mathematics behind deep learning. It focuses on getting you to understand how everything works first which is very important for you to easily catch up on the mathematics later on. There are mathematics involved but they are limited with the sole aim to enhance your understanding and provide a gentle learning curve for future courses that would dive much deeper into it.
Latest Python Notebooks Compatible with PyTorch 0.4 and 1.0
There are very small changes from PyTorch 0.3 for this deep learning series where you will find it is extremely easy to transit over!
Who this course is for:
- Anyone who wants to learn deep learning
- Deep learning researchers using other frameworks like TensorFlow, Keras, Torch, and Caffe
- Any python programmer
Instructor
- 4.0 Instructor Rating
- 1,716 Reviews
- 6,765 Students
- 1 Course
Currently I am leading artificial intelligence with my colleagues in ensemblecap, an AI hedge fund based in Singapore comprising quants and traders from JPMorgan and Nomura. I have built the whole AI tech stack in a production environment with rigorous time-sensitive and fail-safe software testing powering multi-million dollar trades daily.
I am also an NVIDIA Deep Learning Institute instructor leading all deep learning workshops in NUS, Singapore and conducting workshops across Southeast Asia.
My passion for enabling anyone to leverage on deep learning has led to the creation of Deep Learning Wizard where I have taught and still continue to teach more than 2000 students in over 60 countries around the world. The course is recognized by Soumith Chintala, Facebook AI Research, and Alfredo Canziani, Post-Doctoral Associate under Yann Lecun, as the first comprehensive PyTorch Video Tutorial.
In my free time, I’m into deep learning research with researchers based in NExT++ (NUS) led by Chua Tat-Seng and MILA led by Yoshua Bengio. I was previously conducting research in meta-learning for hyperparameter optimization for deep learning algorithms in NExT Search Centre that is jointly setup between National University of Singapore (NUS), Tsinghua University and University of Southampton led by co-directors Prof Tat-Seng Chua (KITHCT Chair Professor at the School of Computing), Prof Sun Maosong (Dean of Department of Computer Science and Technology, Tsinghua University), and Prof Dame Wendy Hall (Director of the Web Science Institute, University of Southampton). During my time there, I managed to publish in top-tier conferences and workshops like ICML and IJCAI.