My name is Ravin Singh and I’m a second-year graduate student at the University of Maryland. My research focus is to understand the inter-dependence degradation mechanisms in Li-ion batteries. I’m trying to create deep neural networks which are guided by physical law. This is a challenging task because their are certain boundary conditions in battery degradation physics that the network should understand and follow before it is trained to predict how battery life will degrade in different usage and operational conditions.
Looking forward to interact with the community and learn from my peers and help them along the way. Stay safe!
Hello Everyone,
I am Ananth from San Jose(SF Bay Area). Despite having attempted completing the earlier versions of the DL4coders book, every single attempt failed. This time, I have a fixed goal of actually developing an App(in the business process automation sector). This is motivating me to complete the course. I got the book to go along with the course. I am loving it thus far. This ecosystem has greatly matured since the last time I attempted the course(Paperspace/Gradient), Colab etc. Also the forum itself is a treasure trove of wealthy information on various approaches to DL problems spanning a wide problem domains.
Hi Everyone,
my name is Johan, I’m french but studying in Erfurt, Germany. I’m currently doing an apprenticeship in Programming and started today the third semester.
I’m really glad I found this course, it’s awesome and I’m looking forward to learn more. I honestly don’t know what I’m going to do with what I learn here but I just find it exciting. And it seems that there is a lot to experiment, so quite some fun in perspective (when it works, as I often encounter bugs…).
I’m Avi, I’m a 4th year undergraduate student at Cal Poly SLO, I’m currently working on a research project sponsored by the PolyGAIT Lab at Cal Poly and an industry partner, where we’re working on developing a path planning and object/anomaly detection system for drones flying over energy production infrastructure. I’ve been coding since I was a kid, and I have some background working on robotics projects, but I’m still very new to deep learning/machine learning. In my classes related to “artificial intelligence” in school I find more of a focus on the theoretical over the application and so this course is helping me get my hands dirty building a bunch of models and applying theoretical concepts to application. I’m also excited to take what I learn here and share it with my peers working on the research project.
I’m excited to contribute to this community and learn from everyone here along the way!
Yes it’s totally fine, but note that we do assume some knowledge of a couple of things that you won’t have done by year 10:
Derivatives / gradients
Logarithms / exponentials
Also, depending on your school, you may not have done matrix multiplication yet.
These are all topics, however, that Khan covers very well. Many adults have forgotten these topics by the times they get around to doing our course - so when I introduce them I mention at the time that it’s something people should check out on Khan if they’re not comfortable with the topic.
Also, I wouldn’t suggest using autonomous vehicles as a 1st project - maybe start by trying to classify individual video frames as “pedestrian”, “car”, “intersection”, etc?.. Once you’ve finished the book, you should be in a position where you can tackle video data.
Hi everyone
I’m Fahime. I’m M.Sc in Medical physics and I have no background in programming and deep learning. I’m so intetested in application of deep learning in medical imaging, radiotherapy and so on. Despite that deep learning resourses and facilities are rare in my country, I’m trying to learn it hard. Many times I’m confused because I’m a beginner in deep learning but I hope one day I learn it.
So happy to be here with you
Hello everyone,
I will follow the invitation to introduce myself. My background is in biomedical engineering and healthcare and rehabilitation technologies where I finished my masters now almost 10 years ago. I have started my career in marketing for a global industry leading company but moved to software development quickly, which is my true passion. My developer experience is in Java, C++, Python (let us not mention C#) where I have worked on projects involving image processing, automation, 3D printing, radiology, dicom, and even AI (however I was not involved in the training or data mining then). In my current project I am designing a UI for an embedded medical device which does not involve deep learning. While I am still curious about deep learning, I chose this course to learn. Thanks to the fast.ai framework, YouTube, paperspace and the kindle ebook I can literally learn about and even do deep learning on my phone whenever there is time. For free!
Thank you!
Michael
Hello, I’m from Saint-Petersburg, Russia and my background is video game industry.
Recently I realized that I’d like to work more with interdisciplinary fields, and things that affect real world more directly than videogames do. ML seems like a thing in that direction with many fun properties.
This is my third dive into ML and I hope with fastai I’ll start using it “for real”.
Make something cool y’all! Cheers.
My name is Russell, I am from Atlanta Georgia in the US. I am an unemployed (thanks Covid) electrical engineer looking to move my career into software and more specifically data science.
First off, thanks for offering this wonderful course and for bringing the power of deep learning to everyone. My name is Ossian, and I’m from Sweden but nowadays I live in California. My background is in computational physics. I love to build numerical codes for scientific applications like simulating how waves propagate throughout the earth during an earthquake. I’m interested in learning how AI can accelerate scientific applications far beyond what we can achieve with classical approaches.
I’m used to approaching these problems by starting from a well-known mathematical model, i.e. partial differential equations (PDEs), like the wave equation, that describes the physical phenomena. While the PDEs are often quite simple to state (you can write them down in much less than a sheet of paper), finding the actual solution is impossible without seeking a numerical approximation. Once you start thinking about how to solve these types of problems using computing, that’s when the challenges begin. There are complexities at all scales imaginable, in both space and time. From geometric complexity to interactions between different physical systems, and so forth. The way I currently approach these problems is via a classical numerical approach. That is, you discretize space by filling it up with points that represent the locations at which you want to obtain the solution. At each of these points, you approximate the PDE. That involves approximating derivatives and that typically requires forming connections between the points in either an unstructured or structured manner. You run into all sorts of interesting issues like making sure that your solution is well-resolved enough for your application and that your method doesn’t cause it to blow up, which can easily happen. Also, depending on the application and numerical approach taken, the resulting computation can become large enough to consume hundreds to thousands of GPUs.
I’m somewhat new to machine learning and deep learning in general, but I’m watching with excitement as these approaches make their way into computational physics. But I don’t want to stand idly by as the developments unfold, I want to be part of it. So, I’m taking my first steps into this exciting field. Today, I joined the deep learning study group, and it is thanks to them that I’m writing this introduction :). If there’s anyone else interested in AI for computational physics, I hope to connect with you. It is always fun to meet new people who share similar interests. If you are interested in following me on my adventure, my Twitter is @ossian_oreilly.
There’s, like, a billion papers & possible applications of ML to PDE solution and more generally to computational physics! You’ll definitely meet your match I (re)tweet about some of this stuff every now and then: https://twitter.com/unsorsodicorda/status/1319626854563794945
Thank you very much for pointing me in the right direction . The paper you linked on Twitter is precisely the type of work I’m interested in learning more about.
I’ll follow you and look forward to seeing more ML PDE work.
Hello
I am a radiologist who inspired so much from Jeremy s last youtube lecture at freecodecamp channel. I suppose radiologists have to be a good model trainer and error analyzer in near future.
After watching video of J.Howard, I have learned that having lots of modified downsampled training dataset is the first step for training an effective model.
So I wrote a code in python for practical bulk converting and downsampling your dicom images to jpeg files. You can try it from the link below DICOMtoJPEGBulkConverter
First of all, thanks for this great course! I really appreciate such a high quality course being available for everyone (for free) online. Keep up the good work!
My name is Aksel and I’m from Amsterdam. Over a decade ago I got a BSc in Artificial Intelligence from the University of Amsterdam. After a few career sways I’m now back to programming again. I recently followed a course on product management for AI which triggered my interest in machine learning again.
I’ve started the course a few weeks ago and I’m now in chapter 6, rebuilding the bear classifier for multi-labels. I really enjoy the hands-on approach to building tangible projects.
Looking forward to building many more interesting applications with FastAI.
Hi everyone, i just got my bachelor’s degree in computer science, gonna pursue a graduate degree now. Just finished the fifth chapter of the book. I’am really motivated and looking forward to help/ contribute/ participate in computer vision related projects .
I’m Paolo from Italy and I’d like to thank you fast.ai and Jeremy for the great knowledge he’s sharing with us.
I’m a graduate in media science and I’m really fascinated about deep learning. I started learning python and data science about one year ago and took some course about ML, accidentally I come up here after googling information on validation sets .
Well, my goal is to gain knowledge and develop a carrier in Data Science and DL, although I don’t have a specific scientific background.
I graduated with Electrical Engineering and worked as a Hardware Validation/Test Engineer for past 10+ years and looking for a career switch into Deep Learning/ Computer Vision. I went through fastai part 1 (2018) version and currently going through the 2020 version. My goal is to get my foot in the door without having taking the traditional university route. If anyone has any tips, it would be greatly appreciated.
I (re)tweet about some of this stuff every now and then: 
. The paper you linked on Twitter is precisely the type of work I’m interested in learning more about.