Now that we have learned about all the problems that can arise when building neural networks and how to deal with them, let's explore how to put all this knowledge into practice. In this video, I will give you some tips on how to best initialize a network.

Previous lesson: https://youtu.be/yizpcnZvvK0
Next lesson: https://youtu.be/NE1iCyqUMRI

Here you will find a booklet of lesson notes that summarizes everything you will 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 panda cheat sheet: https://misraturp.gumroad.com/l/pandascs
Streamlit template (updated 2023, now $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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