Part 3: Training the Neural Network @misraturp

Uploaded November 2022 | Updated September 2026, 3 hours ago

Now that we learned about all the issues that can arise when building g Neural Networks and how to deal with them, we will look into how to put all this knowledge into practical use. In this exercise, we will train and evaluate the network we have been building!

Previous lesson: youtu.be/GrCxB87PA64
Next lesson: youtu.be/yizpcnZvvK0

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

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

❓To get the most out of the course, don't forget to answer the end of module questions:
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

RESOURCES:
🏃‍♀️ Data Science Kick-starter mini-course: misraturp.gumroad.com/l/kick-starter
🐼 Pandas cheat sheet: misraturp.gumroad.com/l/pandascs
📙 Fundamentals of Deep Learning in 25 pages: misraturp.gumroad.com/l/fdl

🌎 Website - misraturp.com
🐥 Twitter - twitter.com/misraturp

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What is Dropout Regularization | How is it different?

Tuning a Neural Network | Deciding on next steps to take

How to Build Your First Neural Network in Python and Keras

Episode 10 - An unexpected way of using data science and life at Adobe with Shivali Goel

Part 1: Getting Ready to Build your First Advanced Neural Network

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Advanced Methods for Hyperparameter Tuning