Forwarded from بینام
Deep Feature Flow for Video Recognition.pdf
3.7 MB
comprehensive-guide.pdf
19.8 MB
A Comprehensive Guide to Machine Learning
By Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang
#book #machinelearning #artificialintelligence
@pythonicAi
By Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang
#book #machinelearning #artificialintelligence
@pythonicAi
HOW TO START MACHINE LEARNING
Pre-requisites:
1⃣ Programming (preferably Python) 2⃣ Linear Algebra
3⃣ Probability and Statistics
Starting Book(s):
1⃣ Pattern Recognition and Machine Learning by Chris Bishop
2⃣ Deep Learning by Goodfellow, Bengio and Courville (For starting Deep Learning)
Course(s) & Certification(s):
1⃣ Coursera Machine Learning by Andrew Ng
2⃣ Coursera Deep Learning Specialization by deeplearning.ai
3⃣ edX Machine Learning by Columbia University
#machinelearning #deeplearning #course #book #artificialintelligence
@pythonicAi
Pre-requisites:
1⃣ Programming (preferably Python) 2⃣ Linear Algebra
3⃣ Probability and Statistics
Starting Book(s):
1⃣ Pattern Recognition and Machine Learning by Chris Bishop
2⃣ Deep Learning by Goodfellow, Bengio and Courville (For starting Deep Learning)
Course(s) & Certification(s):
1⃣ Coursera Machine Learning by Andrew Ng
2⃣ Coursera Deep Learning Specialization by deeplearning.ai
3⃣ edX Machine Learning by Columbia University
#machinelearning #deeplearning #course #book #artificialintelligence
@pythonicAi
lnkd.in
LinkedIn
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Mathematics For Machine Learning
The table of contents breaks down as follows:
Part I: Mathematical Foundations
- Introduction and Motivation
- Linear Algebra
- Analytic Geometry
- Matrix Decompositions
- Vector Calculus
- Probability and Distribution
- Continuous Optimization
Part II: Central Machine Learning Problems
- When Models Meet Data
- Linear Regression
- Dimensionality Reduction with Principal Component Analysis
- Density Estimation with Gaussian Mixture Models
- Classification with Support Vector Machines
https://mml-book.github.io
#machinelearning #artificialintelligence #book
@pythonicAi
The table of contents breaks down as follows:
Part I: Mathematical Foundations
- Introduction and Motivation
- Linear Algebra
- Analytic Geometry
- Matrix Decompositions
- Vector Calculus
- Probability and Distribution
- Continuous Optimization
Part II: Central Machine Learning Problems
- When Models Meet Data
- Linear Regression
- Dimensionality Reduction with Principal Component Analysis
- Density Estimation with Gaussian Mixture Models
- Classification with Support Vector Machines
https://mml-book.github.io
#machinelearning #artificialintelligence #book
@pythonicAi
Forwarded from DLeX: AI Python (Farzad 🦅)
Springer Ebooks.pdf.pdf
693.5 KB
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#کتاب
#book
❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
جلوی هر کتاب لینک وجود دارد. روی آن کلیک کنید و دانلود کنید. در استفاده از آن به هیچ وجه تردید نکنید، خیلی از آنها بسبار گران هستند و در این ایام خاص رایگان است.
#کتاب
#book
❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
Mathematics for Machine Learning
https://mml-book.github.io/book/mml-book.pdf
#math #machinelearning #artificialintelligence #book
@pythonicAi
https://mml-book.github.io/book/mml-book.pdf
#math #machinelearning #artificialintelligence #book
@pythonicAi
Forwarded from Pythonic AI (Soroush Hashemi far)
Mathematics for Machine Learning
https://mml-book.github.io/book/mml-book.pdf
#math #machinelearning #artificialintelligence #book
@pythonicAi
https://mml-book.github.io/book/mml-book.pdf
#math #machinelearning #artificialintelligence #book
@pythonicAi