Develop Successful Deep Learning Projects with this 10 Steps formula
Following are the 10 steps you can follow for a Deep Learning project success.
1. Understand & Define The problem: Understand the problem first and then define it clearly and you are ready now
2. Prepare your data: It’s all about your data, make sure it ready for the real problems
3. Choosing right method: You will not get it right at first time, still you need something to be on right track
4. Applying Baseline Approach: Use subset of the problem and use simple estimator to get feet wet
5. Using Appropriate Architecture: It’s like selecting adequate weapon depending to the fight you are in
6. Appropriate Training Strategy: Choose the best fit training strategy and apply hyperparameter tuning
7. Stop Overfitting with Regularization: Build real learning solution, not the one which cheat through learning
8. Enhance through Augmentation: Add various data into the mix to enhance your results
9. Add Model Interpretability: Stay away from Black box results by adding proper model understanding
10. Deploy and Monitor Model: Consume your model with its deployment and monitor it
== Video Timeline ==
(00:00) Content Intro
(01:25) 1. Understand & Define The problem
(03:03) 2. Prepare your data
(04:45) 3. Choosing right method
(07:00) 4. Applying Baseline Approach
(08:08) 5. Using Appropriate Architecture
(10:20) 6. Appropriate Training Strategy
(13:20) 7. Stop Overfitting with Regularization
(14:50) 8. Enhance through Augmentation
(16:50) 9. Add Model Interpretability
(18:20) 10. Deploy and Monitor Model
(21:15) Conclusion
Please visit:
https://prodramp.com | @prodramp
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Content Creator:
Avkash Chauhan (@avkashchauhan)
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Tags:
#generativeart #stabilitydiffusion #blender3d #chatgpt #promptengineering #llm #stabilityai
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