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@ana_lauraksz: 🩵#fyppppppppppppppppppppppp #dicasdebeleza #dicas #arrumesecomigo #clashroyale

𝓐𝓷𝓪 𝓚𝓼
𝓐𝓷𝓪 𝓚𝓼
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Region: BR
Thursday 18 December 2025 16:59:54 GMT
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Comments

ivgmes.44
nicole :
arrasou amg, faça mais vídeos!♡
2026-03-02 00:14:02
0
w3tt7_
🧜🏼‍♀️ :
acompanho a diva sempre,me inspiro muito e ela sempre me ajuda!!
2025-12-18 18:00:58
4
najuzinhadias
Ana julia 🍀 :
Pura verdade isso de queratina depois que começei a usar no cabelo melhoro muitoooo
2025-12-18 21:29:00
1
4lbqueeqr
4lbqueeqr :
pq colocou aparelho?
2025-12-24 02:26:38
0
ecbrj0
telinha :
faz mais vídeos assim amiga 😍😍
2025-12-18 17:40:06
1
julia_ilic
julia_ilic :
Ameiii
2025-12-18 20:57:31
1
_vv.mari
duda :
Faz mais vídeos assim Ana,amei muito! ❤️
2025-12-19 20:06:17
1
ivgmes.44
nicole :
amg, da dicas de make pra quem é iniciante 🥺💕
2026-03-02 00:16:33
0
camila.8u
camila💓 :
amei as dicas😍
2025-12-18 17:20:57
1
m4nu.nt
m4nu :
ameii amg
2025-12-18 18:54:23
1
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Other Videos

Linear Discriminant Analysis from Scratch using Python: A Mathematical Approach Building a complete Linear Discriminant Analysis implementation in pure Python. From data preparation to projection matrices, exploring mathematical foundations and real-world applications. Discover how LDA can be used for dimensionality reduction and classification tasks without external libraries. You can find, for free, this and all others slideshow on the xbe.at website #python #computerscience #datascience #machinelearning #mathematics #programming #coding #stem #algorithms #Tech #statistics #dataanalysis Key points to strengthen your LDA implementation journey: 1. Start with the mathematical foundations. Take detailed notes about scatter matrices, eigenvalues, and projections. Understanding the math behind LDA will help you implement better solutions and debug issues effectively. 2. Test extensively with different datasets. Each dataset brings unique challenges - some might have outliers, others might have non-linear patterns. Document how your implementation handles these cases. 3. Break down the implementation into logical components. Handle data preprocessing, matrix calculations, and projections separately. Test each component thoroughly before integration. 4. Validate results against known cases. Compare your implementation with simple datasets where you can manually calculate the expected results. Small datasets help verify correctness. 5. Focus on numerical stability. Matrix operations can be sensitive to floating-point errors. Implement checks for singular matrices and near-zero values. Consider using techniques like regularization.
Linear Discriminant Analysis from Scratch using Python: A Mathematical Approach Building a complete Linear Discriminant Analysis implementation in pure Python. From data preparation to projection matrices, exploring mathematical foundations and real-world applications. Discover how LDA can be used for dimensionality reduction and classification tasks without external libraries. You can find, for free, this and all others slideshow on the xbe.at website #python #computerscience #datascience #machinelearning #mathematics #programming #coding #stem #algorithms #Tech #statistics #dataanalysis Key points to strengthen your LDA implementation journey: 1. Start with the mathematical foundations. Take detailed notes about scatter matrices, eigenvalues, and projections. Understanding the math behind LDA will help you implement better solutions and debug issues effectively. 2. Test extensively with different datasets. Each dataset brings unique challenges - some might have outliers, others might have non-linear patterns. Document how your implementation handles these cases. 3. Break down the implementation into logical components. Handle data preprocessing, matrix calculations, and projections separately. Test each component thoroughly before integration. 4. Validate results against known cases. Compare your implementation with simple datasets where you can manually calculate the expected results. Small datasets help verify correctness. 5. Focus on numerical stability. Matrix operations can be sensitive to floating-point errors. Implement checks for singular matrices and near-zero values. Consider using techniques like regularization.
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