Foundations of Applied Mathematics is a free series of four textbooks created for the applied and computational mathematics program at Brigham Young University. 📚
The series includes four volumes:
* Mathematical Analysis
* Algorithms, Approximation, and Optimization
* Uncertainty and Data
* Dynamics and Control
The series is suitable for upper-level undergraduate and introductory graduate students. It also includes Python lab exercises and practical assignments, connecting mathematical theory with numerical computation, algorithms, data analysis, and scientific applications. 🐍
I particularly appreciate that these are not just theoretical textbooks. The accompanying Python materials help to illustrate how these concepts are applied to real-world computational problems. 💻
https://foundations-of-applied-mathematics.github.io
#Mathematics #Python #Education #DataScience #Algorithms #Learning
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The series includes four volumes:
* Mathematical Analysis
* Algorithms, Approximation, and Optimization
* Uncertainty and Data
* Dynamics and Control
The series is suitable for upper-level undergraduate and introductory graduate students. It also includes Python lab exercises and practical assignments, connecting mathematical theory with numerical computation, algorithms, data analysis, and scientific applications. 🐍
I particularly appreciate that these are not just theoretical textbooks. The accompanying Python materials help to illustrate how these concepts are applied to real-world computational problems. 💻
https://foundations-of-applied-mathematics.github.io
#Mathematics #Python #Education #DataScience #Algorithms #Learning
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A Powerful Alternative to Pandas 🚀
This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. ⚙️
To get started, simply replace a single import:
Performance Benchmarks demonstrate speed improvements in various use cases. 📈
More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing
#Pandas #Python #DataScience #Performance #Fireducks #BigData
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This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. ⚙️
To get started, simply replace a single import:
import fireducks.pandas as pd
Performance Benchmarks demonstrate speed improvements in various use cases. 📈
More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing
#Pandas #Python #DataScience #Performance #Fireducks #BigData
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Day 7 of self-studying Berkeley CS189 — stochastic gradient descent notes 📚📝
🔥 *Stochastic Gradient Descent (SGD)* is a powerful optimization algorithm used to minimize loss functions in machine learning. Unlike batch gradient descent, which uses the entire dataset to compute gradients, SGD updates parameters using a single training example (or a small mini-batch) at a time.
🚀 Key Benefits:
- Faster convergence on large datasets
- Escapes local minima more easily
- Suitable for online learning scenarios
📊 The Update Rule:
Where
📌 Challenges:
- High variance in updates
- Requires careful tuning of the learning rate
🧠 *Tip:* Use momentum or adaptive learning rates (like Adam) to stabilize training!
#MachineLearning #CS189 #SGD #DeepLearning #DataScience #Algorithms
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🔥 *Stochastic Gradient Descent (SGD)* is a powerful optimization algorithm used to minimize loss functions in machine learning. Unlike batch gradient descent, which uses the entire dataset to compute gradients, SGD updates parameters using a single training example (or a small mini-batch) at a time.
🚀 Key Benefits:
- Faster convergence on large datasets
- Escapes local minima more easily
- Suitable for online learning scenarios
📊 The Update Rule:
θ = θ - α * ∇J(θ; x⁽ⁱ⁾, y⁽ⁱ⁾)Where
α is the learning rate and (x⁽ⁱ⁾, y⁽ⁱ⁾) is a single training example.📌 Challenges:
- High variance in updates
- Requires careful tuning of the learning rate
🧠 *Tip:* Use momentum or adaptive learning rates (like Adam) to stabilize training!
#MachineLearning #CS189 #SGD #DeepLearning #DataScience #Algorithms
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🔖 Learning Data Science through interactive examples
One of the most useful repositories for those who want to better understand machine learning.
It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results.
⛓ Link to GitHub
https://github.com/GeostatsGuy/DataScienceInteractivePython
#DataScience #MachineLearning #Python #Learning #Tech #GitHub
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One of the most useful repositories for those who want to better understand machine learning.
It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results.
⛓ Link to GitHub
https://github.com/GeostatsGuy/DataScienceInteractivePython
#DataScience #MachineLearning #Python #Learning #Tech #GitHub
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🔖 Over 300 real-world case studies of ML systems from top companies. 🤖
We found a repository that collects genuine ML engineering experience – not theory from textbooks, but real stories of implementing models in production. 📚
Inside, you'll find case studies from Uber, Netflix, Google, and other companies: how they built the architecture, what problems arose, where the systems failed, and what solutions helped them recover. 🏗️
⛓ Link to GitHub
https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies
#MachineLearning #MLCaseStudies #DataScience #Engineering #Uber #Netflix
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We found a repository that collects genuine ML engineering experience – not theory from textbooks, but real stories of implementing models in production. 📚
Inside, you'll find case studies from Uber, Netflix, Google, and other companies: how they built the architecture, what problems arose, where the systems failed, and what solutions helped them recover. 🏗️
⛓ Link to GitHub
https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies
#MachineLearning #MLCaseStudies #DataScience #Engineering #Uber #Netflix
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🚀 TOP 8 Machine Learning Regression Metrics Explained
Choosing the right metric isn't academic; it's the difference between a model that works in production and one that breaks trust.
Here's the map every ML engineer should carry in 2026:
1️⃣ MEAN ABSOLUTE ERROR (MAE)
Average miss, easy to explain. On average, we're off by 5 units.
2️⃣ MEAN SQUARED ERROR (MSE)
Squares mistakes → big errors hurt more.
3️⃣ ROOT MEAN SQUARED ERROR (RMSE)
Square root of MSE. Same unit as the target, easier to relate.
4️⃣ R² COEFFICIENT
Explains how much variation your model captures. But don't confuse fit with usefulness.
5️⃣ ADJUSTED R²
Keeps R² honest. Extra useless features won't inflate the score.
6️⃣ MAPE (Mean Absolute Percentage Error)
Errors in percentages. Great for business dashboards, weak if actual values get near zero.
7️⃣ Huber Loss
Blends MAE & MSE. Punishes small errors like MSE, resists outliers like MAE.
8️⃣ Quantile Loss
Perfect when predicting ranges instead of single points like demand at the 90th percentile.
👁 VIEW
● = Actuals ○ = Predictions
MAE → avg |●-○|
MSE → avg (●-○)²
RMSE → √MSE
R² → variance explained
MAPE → % error
Huber → balance (MSE + MAE)
Quant → percentile accuracy
🏆 THE TAKEAWAY
Metrics decide what success looks like.
Choose wrong, and your good model is useless.
Choose right, and you build trust, adoption, and impact.
📝 TL;DR
MAE → simple error
MSE → punishes big errors
RMSE → interpretable scale
R² → fit, not prediction power
Adj R² → guards against overfitting
MAPE → % view, fragile near zero
Huber → outlier-resistant
Quantile → forecasts ranges
#MachineLearning #DataScience #RegressionMetrics #MLOps #AI #TechTips
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Choosing the right metric isn't academic; it's the difference between a model that works in production and one that breaks trust.
Here's the map every ML engineer should carry in 2026:
1️⃣ MEAN ABSOLUTE ERROR (MAE)
Average miss, easy to explain. On average, we're off by 5 units.
2️⃣ MEAN SQUARED ERROR (MSE)
Squares mistakes → big errors hurt more.
3️⃣ ROOT MEAN SQUARED ERROR (RMSE)
Square root of MSE. Same unit as the target, easier to relate.
4️⃣ R² COEFFICIENT
Explains how much variation your model captures. But don't confuse fit with usefulness.
5️⃣ ADJUSTED R²
Keeps R² honest. Extra useless features won't inflate the score.
6️⃣ MAPE (Mean Absolute Percentage Error)
Errors in percentages. Great for business dashboards, weak if actual values get near zero.
7️⃣ Huber Loss
Blends MAE & MSE. Punishes small errors like MSE, resists outliers like MAE.
8️⃣ Quantile Loss
Perfect when predicting ranges instead of single points like demand at the 90th percentile.
👁 VIEW
● = Actuals ○ = Predictions
MAE → avg |●-○|
MSE → avg (●-○)²
RMSE → √MSE
R² → variance explained
MAPE → % error
Huber → balance (MSE + MAE)
Quant → percentile accuracy
🏆 THE TAKEAWAY
Metrics decide what success looks like.
Choose wrong, and your good model is useless.
Choose right, and you build trust, adoption, and impact.
📝 TL;DR
MAE → simple error
MSE → punishes big errors
RMSE → interpretable scale
R² → fit, not prediction power
Adj R² → guards against overfitting
MAPE → % view, fragile near zero
Huber → outlier-resistant
Quantile → forecasts ranges
#MachineLearning #DataScience #RegressionMetrics #MLOps #AI #TechTips
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Forwarded from Udemy Free
Python And Flask Framework Complete Course
Python-Powered Proficiency: Depth Introduction To Python Programming And Python Web Framework Flask.…
🏷 Category: it-and-software
🌍 Language: English (US)
👥 Students: 292,248 students
⭐️ Rating: 4.5/5.0 (2,187 reviews)
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#Python #DataScience #Automation #FreeCourse #Udemy #OnlineLearning
Python-Powered Proficiency: Depth Introduction To Python Programming And Python Web Framework Flask.…
🏷 Category: it-and-software
🌍 Language: English (US)
👥 Students: 292,248 students
⭐️ Rating: 4.5/5.0 (2,187 reviews)
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⏳ Expires In: N/A
💰 Price:
🆔 Coupon:
25BBPMXNVD35⚡ Opens instantly — your free link unlocks on its own in seconds, no ad required.
💎 By: https://xn--r1a.website/Udemy26
#Python #DataScience #Automation #FreeCourse #Udemy #OnlineLearning
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"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. 📚🧮
The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. 🤖📈
A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). 🎲🧠
The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊
In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." 💡✨
https://arxiv.org/pdf/2409.02668
#MachineLearning #DeepLearning #AI #Mathematics #DataScience #NeuralNetworks
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The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. 🤖📈
A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). 🎲🧠
The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊
In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." 💡✨
https://arxiv.org/pdf/2409.02668
#MachineLearning #DeepLearning #AI #Mathematics #DataScience #NeuralNetworks
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Forwarded from Machine Learning with Python
DS FULL ARCHIVE.pdf
37.1 MB
DS FULL ARCHIVE 🗂
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