Top Tweets for #MathForML
https://t.co/ogFIPGFh7s
This book : Mathematics for machine learning
from :
Marc Peter Deisenroth,
A. Aldo Faisal,
Cheng Soon Ong. !
#MachineLearning #MathForMl
Day 15/365 — mean = common sense.
Average marks.
Average salary.
Average height.
It’s just: Total ÷ count.
Understood it manually first.
Then used Python.
Meaning > speed.
#LearningInPublic #365DaysOfLearning
#StatisticsBasics #MathForML
#ConceptFirst
from today the math grind is starting. i'm going to learn probability, statistics, linear algebra and calculus as fast as possible but also going to understand it to implement it in my ml learning journey
#mathforml
Day 10 Learning Log
📅 Jan 24
📊 Multivariate Statistical Analysis
MANOVA
Wilks’ Lambda
Pillai’s Trace
MANCOVA
#LearningLog #Statistics #MultivariateAnalysis #MANOVA #MANCOVA
#WilksLambda #PillaisTrace #DataScience #MachineLearning
#Analytics #StatLearning #MathForML
🚀 Looking for FREE channels to learn Math for Machine Learning
I’m searching for good YouTube / free resources to learn:
• Linear Algebra
• Probability & Statistics
• Calculus (ML-focused)
Your recommendations could help me:
#MachineLearning #MathForML #AI #DataScience
Started Linear algebra and it's going good.
> Matrix transform
> Gaussian elimination
> Row Echelon form
> Orthogonality
> Solution of linear systems of equations
#mathforml #GenAI #ai #LearnInPublic
📘 Grab the free “Mathematics for Machine Learning” book and strengthen your ML foundations in linear algebra, calculus, probability, and optimization.
Download: https://t.co/vtfGo1MgLq
#MachineLearning #MathForML #AI #DataScience #MLBook
Next up → Essence of Calculus.
Time to understand how optimization actually “learns.”
Quietly building foundations strong enough to carry the weight of everything I want to create later.
#MachineLearning #AI #MathForML #3Blue1Brown #LearningInPublic
Understanding symmetric matrices and positive definiteness is key to mastering #MachineLearning.
They explain why algorithms like gradient descent actually converge!
🎥 Watch here → https://t.co/LJsDWZ15o3
#MathForML #AI #DeepLearning #LinearAlgebra
Day 26 of #MathForML
Today's labwork: Applications of EigenValues & EigenVectors
-> Navigating Webpages
-> PCA on cat images
Before diving into calculus, I want to make sure my Linear Algebra is rock solid.
Can you suggest resources to master and practice Linear Algebra for ML?
Dot product sounds scary — until you see it for what it really is:
👉 A way to measure alignment
👉 A key to cosine similarity
👉 The engine behind transformers
Here’s what I learned today about this tiny but mighty math tool 🧠👇
#MLZoomcamp #mathforml #dotproduct
- Dimension = number of basis vectors
💻 In PyTorch:
torch.linalg.matrix_rank(mat) # dimension of span
💡 Insight: More vectors ≠ higher dimension — redundancy keeps you in the same space.
https://t.co/KqiyAUC6rj
#LinearAlgebra #PyTorch #BackendToAI #MathForML
✨ What is the Dot Product & why it matters in ML
Multiply matching vector elements ➕ sum them up:
A · B = |A||B|cos(θ)
🔹 Measures similarity
🔹 Powers neural nets, attention, and cosine similarity
Tiny math → big insights!
#Day77 of #NeuralNetworkJourney #MathForML #DotProduct #AI #LinearAlgebra
Just found @geogebra — one of the best tools I’ve seen for understanding math visually.
Super helpful for grasping ML topics like vectors, functions, and calculus.
🔗 https://t.co/wwAWTL5TT0
Definitely worth checking out!
#MathForML #GeoGebra #LearnInPublic
Day 54 #MyDataScienceJourney: Covered equations of lines, 3D planes, and hyperplanes, plus how to find a point’s distance from a plane. Also explored instance-based vs. model-based learning in ML—fascinating to see how model either memorize or generalize!#LearnInPublic #MathForML
🧠 Solving A·X = B in ML
🔹 Matrix Inverse:
X = A⁻¹·B (only if A is invertible)
🔹 Gaussian Elimination:
Systematically reduces equations → solution
This math powers linear regression, optimization & neural nets!
#Day75 of #NeuralNetworkJourney #MathForML #AI #LinearAlgebra #GaussianElimination
🧾 Matrix Form of Systems in ML
A system like:
2x + 3y = 5
4x + y = 6
Becomes:
👉 A·X = B
Where A = coefficients, X = variables, B = outputs
Fast, scalable — perfect for machine learning!
#Day74 of #NeuralNetworkJourney #AI #ML #MathForML #LinearAlgebra #Matrices
🧮 How is Algebra used in Machine Learning?
It’s the foundation:
🔹 Equations model relationships (like y = mx + b)
🔹 Vectors & matrices represent data
🔹 Solving systems = optimizing models
ML = data + math + logic 💡
#Day72 of #NeuralNetworkJourney #ML #AI #MathForML #Algebra
Stop wondering why your model’s not improving, it's not stuck, it's just converging slowly.
📘 Understand the math behind the motion → https://t.co/XwpnuQYYOh
#LinearityOfConvergence #MathForML #100DaysOfMathematicsOfML
Last Seen Hashtags on Sotwe
Trends for you
Most Popular Users
Elon Musk 
@elonmusk
240.1M followers
Barack Obama
@barackobama
119.3M followers
Donald J. Trump
@realdonaldtrump
111.6M followers
Cristiano Ronaldo
@cristiano
108.8M followers
Narendra Modi
@narendramodi
107M followers
Rihanna
@rihanna
97.2M followers
NASA
@nasa
92.1M followers
Justin Bieber
@justinbieber
90.5M followers
KATY PERRY
@katyperry
86.7M followers
Taylor Swift
@taylorswift13
80.5M followers
Lady Gaga
@ladygaga
72.1M followers
Kim Kardashian
@kimkardashian
69.4M followers
YouTube
@youtube
68.6M followers
Virat Kohli
@imvkohli
68.5M followers
Bill Gates
@billgates
63.4M followers
The Ellen Show
@theellenshow
62.5M followers
CNN
@cnn
61.9M followers
Neymar Jr
@neymarjr
61M followers
X
@x
60.9M followers
CNN Breaking News
@cnnbrk
59.9M followers