Neural networks and neural network-based architectures are powerful models that can handle abstract problems, but are notoriously time-consuming to train. In this video, we take a high-level look at what kinds of solutions are available to reduce the amount of time it takes for a neural network to train.

Previous lesson: https://youtu.be/eMmk_C8yf2U
Next lesson: https://youtu.be/jL4cs5EZuO4

Here you will find a lesson notes booklet that summarizes everything you learn in this course in diagrams and visualizations. You can get it here https://misraturp.gumroad.com/l/fdl

‍ You can access all the code I develop in this course here: https://github.com/misraturp/Deep-learning-course-repo

To get the most out of the course, don't forget to answer the questions at the end of the module:
https://fishy-dessert-4fc.notion.site/Deep-Learning-101-Questions-35797462583d43a3adcea9478bfe035d

You can find the answers here:
https://fishy-dessert-4fc.notion.site/Deep-Learning-101-Answers-f90449595add492fb9dd8db3859662e3

SOURCES:
‍️ Data Science Kick-starter mini-course: https://www.misraturp.com/courses/data-science-kick-starter-mini-course
The cheat sheet for pandas: https://misraturp.gumroad.com/l/pandascs
Streamlit Template (updated 2023, now for 5): https://misraturp.gumroad.com/l/stemp
NNs hyperparameters cheat sheet: https://www.misraturp.com/nn-hyperparameters-cheat-sheet
Fundamentals of Deep Learning in 25 pages: https://misraturp.gumroad.com/l/fdl

COURSES:
‍ Practical Data Science: Complete Your First Portfolio Project: https://www.misraturp.com/hods

Website – https://misraturp.com/
Twitter – https://twitter.com/misraturp

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