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
Coursera
Machine Learning by Stanford University | Coursera
Learn Machine Learning from Stanford University. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, ...
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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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
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