GenAI Infused Machine Learning Library for Easy Preprocessing, Training and Testing

~ !pip install autotuner ~ !pip install automaker !pip install autotuner-min

!pip install autotuner

!pip install deep-analysis

June 27th

• CNN - https://cs.nju.edu.cn/wujx/paper/CNN.pdf
• CNN for Visual Recognition - https://cs231n.github.io/optimization-2/
• Backpropagation - http://neuralnetworksanddeeplearning.com/chap2.html
• A Neural Network in 13 lines of Python (Part 2 - Gradient Descent) - https://iamtrask.github.io/2015/07/27/python-network-part2/
• Machine Learning Crash Course - https://ml.berkeley.edu/blog/posts/crash-course/part-3/
• Understanding CNN with a mathematical model - https://www.sciencedirect.com/science/article/abs/pii/S1047320316302267
• CNN Stufy guide - https://statquest.org/studyguides/
• Evaluation of pooling operations in convolutional architectures for object recognition - https://link.springer.com/chapter/10.1007/978-3-642-15825-4_10
• CNN 2D Viosualization - https://www.cs.ryerson.ca/~aharley/vis/conv/flat.html
• The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) - https://adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html
• A Friendly Introduction to Cross-Entropy Loss - https://rdipietro.github.io/friendly-intro-to-cross-entropy-loss/
• Softmax classification with cross-entropy - https://peterroelants.github.io/posts/cross-entropy-softmax/
• The Ultimate Guide to Convolutional Neural Networks (CNN) - https://www.superdatascience.com/blogs/the-ultimate-guide-to-convolutional-neural-networks-cnn

• QwQ 27 B
• QwQ 28 Billion (8bit Quant)

• Llama 4 - 1T prams

• Report Gen (2024)

• EARTHDATA - https://earthdata.nasa.gov/
• Dataset Search - https://datasetsearch.research.google.com/
• Crime Data Explorer - https://crime-data-explorer.fr.cloud.gov/
• Data World - https://data.world/
• CERN - https://home.cern/
• Lion Bridge - https://www.lionbridge.com/
• Honorable Mentions - (AwesomeData) - https://github.com/awesomedata

RoadMap: https://whimsical.com/machine-learning-roadmap-2020-CA7f3ykvXpnJ9Az32vYXva

ML - Learning Phase

1. Data Preprocessing https://srivicky2000.medium.com/data-preprocessing-in-python-e6c61b051dc9
   

2. Difference Between Independent and Dependent Variable https://srivicky2000.medium.com/difference-between-independent-and-dependent-variable-18aa09e5a214

3. Multiple Linear Regression in Python (Lateral Acceleration) https://srivicky2000.medium.com/multiple-linear-regression-in-python-e093abd12430

4. Polynomial Regression https://srivicky2000.medium.com/polynomial-regression-in-python-c082c698d081 MLR https://gist.github.com/ashwath007/c895883d26a289cdedbd69001b0dc35f.js

5. Linear Regression in 2 min https://srivicky2000.medium.com/simple-linear-regression-in-2-min-acaf2f523369

6. R^2 in Regression https://srivicky2000.medium.com/what-is-r%C2%B2-in-regression-b62bb3ec848f

7. SVM- SVR https://srivicky2000.medium.com/svm-support-vector-machine-fa897f1e5187

8. SVM - SVC Support Vector Classification https://srivicky2000.medium.com/svm-support-vector-machine-2a82fd8d15bf

9. Gradient Descent and Normal Equation https://dj-sri-vigneshwar.medium.com/normal-equation-or-gradient-descent-e9d9b9345879

10. The math behind Naive Bayes https://dj-sri-vigneshwar.medium.com/the-math-behind-naive-bayes-4e5791ec6f22 towardsdatascience

11. Understanding— Logistic Regression within 5min 🐱‍🏍 - https://srivicky2000.medium.com/understand-logistic-regression-within-5min-84c329bff97

• Week 2 - https://github.com/amanchadha/coursera-deep-learning-specialization/blob/master/C1%20-%20Neural%20Networks%20and%20Deep%20Learning/Week%202/Logistic%20Regression%20as%20a%20Neural%20Network/Logistic_Regression_with_a_Neural_Network_mindset_v6a.ipynb

Multimodel - 7 Models

I am studying Computer Science Engineering, I have a solid background in Math and Programming, and I love problem-solving. And I have a keen interest in ML and Deep Learning. This course will provide in-depth knowledge and programming skill to pursue in the Machine Learning path. This course certificate will increase my chances to get my first job or internship. I always wanted to be a part of Deep Learning Research and Development, This course will give me everything for that. I am a hard-working student, who learns every day and upgrades his skill, This course will bring a difference in my life. I wanted to be an expert in Machine Learning and Deep Learning. I believe this course will be the foundation for my career. As an engineering student, I always have an urge to solve tough problems. With Deep Learning I could approach and solve it. I am really interested in Self-driving cars, Noise Cancellation tech, I am looking forward to dive into it.