Henok
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Henok here. Just a messy collection of interesting things to improve or make your life worse!
Reach me at @StoicallyAwake.
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I have so much to improve apparently but-
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How far can trenches of grief be?

Regret and grief, one born of our own missteps, the other of life's cruel whims, beyond our control. Regrets sting, but grief is a wound akin to death itself. It's witnessing the world, vibrant yet untouchable, and being helpless to mend what's broken; a mother watching her son march off to war, never to return. Its depths can be even darker: a love so profound it's carried in our very bones, yet choosing to let it go, the ultimate surrender.

Any soul who has lived long enough has known the taste of regret, the weight of grief. But it is those who endure, who weather these storms, who find a light in the deepest darkness, that truly learn to live. And what of those who dwell in shadows, forever haunted by "what could have been"? They hold their memories close, a quiet ache in their hearts, but rise each day to meet the sun.

For even in the darkest trenches of grief, a wildflower can bloom. It may be small, fragile, but it perseveres, drawing strength from the very earth that holds the pain. And like that wildflower, even those bearing grief carry love and joy and a bittersweet beauty within them.


#MyPoetries
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Not Like Us
Kendrick Lamar
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Sometimes you gotta pop-out and show niggas.

-Kendrick
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Media is too big
VIEW IN TELEGRAM
Walter Lewin😍
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Machine Learning (ML) Roadmap
Mathematical Foundations (Must know before ML)

1. Algebra
2. Trigonometry
3. Basic Geometry
4. Precalculus (including complex numbers)
5. Linear Algebra
6. Single-Variable Calculus
7. Multivariable Calculus
8. Probability & Statistics
9. Optimization

Programming & Computational Skills

1. Python (NumPy, Pandas, Matplotlib, SciPy)
2. Data Structures & Algorithms (optional)
3. Object-Oriented Programming (OOP)
4. Git/GitHub

Core ML Concepts

1. Supervised & Unsupervised Learning
2. Regression Models (Linear, Logistic)
3. Classification Models (Decision Trees, SVMs, k-NN)
4. Clustering (K-means, DBSCAN)
5. Neural Networks (MLP, Backpropagation)
6. Deep Learning (CNNs, RNNs, Transformers)
7. Feature Engineering & Data Preprocessing
8. Model Evaluation & Metrics
Advanced Topics

1. Optimization for Deep Learning
2. Probabilistic Graphical Models
3. Reinforcement Learning
4. Generative Models (GANs, VAEs)
5. AI Ethics & Bias Mitigation
6. Scalable ML (Big Data, Distributed Training)
7. Mathematical ML (Kernel Methods, Info Theory)ο»Ώ


Practical Applications

1. Computer Vision
2. Natural Language Processing (NLP)
3. Robotics & Autonomous Systems
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The mathematical preliminaries can be omitted if you are looking to just learn practical ML.
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Forwarded from Henok
Geron_A_Hands_on_Machine_Learning_with_Scikit_Learn,_Keras,_and.pdf
60.2 MB
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by AurΓ©lien GΓ©ron
   - This book is widely acclaimed for its practical approach, offering hands-on tutorials and real-world examples.

#Books #ML #AI
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Henok
Geron_A_Hands_on_Machine_Learning_with_Scikit_Learn,_Keras,_and.pdf
And most of the topics mentioned are discussed on this book at basic-intermediate level.
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bishop.pdf
17.3 MB
This is a detailed introduction to the field of pattern recognition and machine learning. At the end of each chapter, there are exercises designed to better explain each concept to the reader.
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Henok
bishop.pdf
This one contains hard core math. It is full of abuse of linear algebra and calc 3.

It teaches you how the algorithms work under the hood.
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Henok
Introduction_to_Linear_Algebra_by_Gilbert_Strang2016,_Wellesley.pdf
Most of the math you will ever need are contanied in these books whether you are into physics, engineering or ML.
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LADR4e.pdf
2.7 MB
Linear algebra done right by Axler
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Henok
LADR4e.pdf
Another alternative to the books above. It is short and concise but has a steeper learning curve.
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