AI Basics 4/10: Model Training - Learn Machine Learning, Full Deep-Dive

Hello! Welcome to Day 4 of the 10 Days of AI Basics. Today, we discuss MODEL TRAINING! If you haven't watched the first 3 videos, definitely make sure you go and watch those first.

Like Day 3, we cover a LOT of topics in this one - check out the timestamp titles. You'll hopefully have learned a LOT by the end of this. Please leave your feedback in the comments! I'd love to hear how this went for you and of any outstanding questions that you have. I will answer them in Day 5.



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Timestamps:
00:00 Intro
01:20 Model Training Overview
03:37 Training vs. Inference/Deployment
04:31 Model Training
11:51 Training, Validation, & Test Sets
14:05 Parameter Space
14:38 Finding the Minimum
16:09 Loss Functions
16:47 Gradient Descent
18:23 Overfitting
20:20 Regularization
20:39 Dropout
22:05 Early Stopping
24:41 Underfitting
26:30 Data Augmentation
26:54 Class Imbalances
27:48 Sampling Techniques
30:08 Transfer Learning
33:19 Fine-tuning
35:28 Hardware Requirements
36:42 Parallelization
38:04 Cloud Compute
39:08 Parameters vs. Hyperparameters
41:43 Hyperparameter Selection
44:19 Conclusion

#AI #ArtificialIntelligence #Learn

[javadsadeghichigani]

Fantasticccc like always My Dear Sister&Pal❤🖐🖐🖐

[TuxedoMaskMusic]

Ty Harper! You are amazing and teach us so much! You are appreciated!

[abdullahrizwan]

Hey harper! I really appreciate the work and thee kind of effort you are putting in to produce these masterpice Ai centered content and I wish you a warm Good luck for your future with content creation❤.
I would commend if you could pinpoint exactly how much and what topics to learn in Maths to be able to take down Ai ML etc.

[Deutschzeit]

It's a very nice video. Can you upload your sample notebooks?