Artificial Intelligence && Deep Learning
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Deep learning lecture
Deep Learning lecture
The full deck of (600+) slides, by Gilles Louppe:


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https://glouppe.github.io/info8010-deep-learning/pdf/lec-all.pdf
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Stanford Machine Learning

Content
01 and 02: Introduction, Regression Analysis and Gradient Descent

03: Linear Algebra - review

04: Linear Regression with Multiple Variables

05: Octave[incomplete]

06: Logistic Regression

07: Regularization

08: Neural Networks - Representation

09: Neural Networks - Learning

10: Advice for applying machine learning techniques

11: Machine Learning System Design

12: Support Vector Machines

13: Clustering

14: Dimensionality Reduction

15: Anomaly Detection

16: Recommender Systems

17: Large Scale Machine Learning

18: Application Example - Photo OCR

19: Course Summary

http://www.holehouse.org/mlclass/

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Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
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Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
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SEVEN NEW COURSES that cover Python, R, and SQL. First up is Analyzing Business Data in SQL, where you’ll learn how to write SQL queries to calculate key business metrics and produce report-ready results. Plus our Introduction to Text Analysis in R course, where you’ll learn how to wrangle and visualize text, perform sentiment analysis, and run and interpret topic models.

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1. 10 New Things I Learnt from fast.ai v3
2. 2019 deep learning course Practical Deep Learning for Coders, v3.

10 learning points as such:

1. The Universal Approximation Theorem
2. Neural Networks: Design & Architecture
3. Understanding the Loss Landscape
4. Gradient Descent Optimisers
5. Loss Functions
6. Training
7. Regularisation
8. Tasks
9. Model Interpretability
10. Appendix: Jeremy Howard on Model Complexity & Regularisation

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https://towardsdatascience.com/10-new-things-i-learnt-from-fast-ai-v3-4d79c1f07e33
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Best Training & Certification Courses for Professionals | Edureka
* PGP in AI & Machine Learning
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