If you’d like to invite your friends or colleagues to join the course and learn together with you, please feel free to use the following email containing the registration instructions and lecture schedule.
Email Subject: Join “Deep Learning with PyTorch: Zero to GANS” - 6-week Online Certification Course
“Deep Learning with PyTorch: Zero to GANs” is an online course that takes a hands-on coding-focused approach to deep learning and will be taught using live interactive Jupyter notebooks. You’ll be able to follow along and experiment with the code, and you can earn a certificate of completion by completing weekly exercises & assignments. This course is free of cost.
STEPS FOR REGISTRATION
This is a 6-week course, consisting of live video lectures, assignments, a course project & a data science competition. The course will be conducted in a private group within the Jovian.ml discussion forum. Please complete the following steps to get started:
Sign up on the Jovian.ml forum: https://jovian.ml/forum/
Join the “Jovian Data Science Network” group (click the “Join” button on the top right)
Make sure you can access the “PyTorch: Zero to GANs” forum category
COURSE SCHEDULE & TIMINGS
Video lectures will be live-streamed on the FreeCodeCamp YouTube channel as per the following schedule:
Lecture 1: May 23 (Sat), 8.30 AM PST/9:00 PM IST
Lecture 2: May 30 (Sat), 8.30 AM PST/9:00 PM IST
Lecture 3: June 6 (Sat), 8.30 AM PST/9:00 PM IST
Lecture 4: June 13 (Sat), 8.30 AM PST/9:00 PM IST
Lecture 5: June 20 (Sat), 8.30 AM PST/9:00 PM IST
Lecture 6: June 27 (Sat), 8.30 AM PST/9:00 PM IST
Add to Calendar (Google)
You can access the full course curriculum here: https://bit.ly/pytorchzerotogans
CERTIFICATE OF COMPLETION
To receive a Certificate of Completion for the course, you’ll need to attend at least 5 out of 6 lectures, and complete all the exercises: 3 weekly assignments, a course project & a data science competition. You can achieve this by spending 8-10 hours per week on the course. More details regarding the certificate will be shared on the course forum and during the first lecture.