Forwarded from Qorpi đť
[email protected]
1.5 MB
đThink Stats: Probability and
Statistics for Programmers
đڊتاب ا٠اع ٠اŘŘŞŮ Ř§Ů Ř¨ŘąŘ§Ű Ř¨ŘąŮا٠٠ŮŮŰساŮ(ڊساŮŰ ÚŠŮ Ř¨ŘŻŮبا٠ŰادگŰŘąŰ ŘŻŰتا ساŰŮŘł ŮستŮŘŻ Řت٠ا اŰ٠ڊتاب عا ب؎ŮاŮŮŘŻ|اŰ٠ڊتاب با زبا٠ٞاŰŘŞŮŮ ŘŞŮŘśŰŘ ŘŻŘ§ŘŻŮ Ř´ŘŻŮ)
#Probability
#Statistics
#Machine_learning
#Python
đťQorpi Academy
AI&ML workgroup
Science workgroup
https://t.iss.one/qorpi
Statistics for Programmers
đڊتاب ا٠اع ٠اŘŘŞŮ Ř§Ů Ř¨ŘąŘ§Ű Ř¨ŘąŮا٠٠ŮŮŰساŮ(ڊساŮŰ ÚŠŮ Ř¨ŘŻŮبا٠ŰادگŰŘąŰ ŘŻŰتا ساŰŮŘł ŮستŮŘŻ Řت٠ا اŰ٠ڊتاب عا ب؎ŮاŮŮŘŻ|اŰ٠ڊتاب با زبا٠ٞاŰŘŞŮŮ ŘŞŮŘśŰŘ ŘŻŘ§ŘŻŮ Ř´ŘŻŮ)
#Probability
#Statistics
#Machine_learning
#Python
đťQorpi Academy
AI&ML workgroup
Science workgroup
https://t.iss.one/qorpi
đđđ° đđđđŠ đđđđŤđ§đ˘đ§đ đđ¨đŽđŤđŹđ! đđđĽł
The NYU Center for Data Science made their Deep Learning course public and shared all the class material (notebook, slides, video lectures). The class is taught by Yann LeCun, VP and Chief AI Scientist at Facebook, and Alfredo Canziani, Professor of Computer Science at New York University.
The course materials are available both in đđ§đ đĽđ˘đŹđĄ đŹđ§and đ đŤđđ§đđĄ đŤđˇ.
The course covers the following topics:
đđ§đđŤđ¨đđŽđđđ˘đ¨đ§
- history of deep learning models
- Gradient descent and the backpropagation algorithm
- Neural nets inference, architect, and training
đđđŤđđŚđđđđŤđŹ đŹđĄđđŤđ˘đ§đ
- Recurrent and convolutional nets
- ConvNets in practice
- Recurrent neural networks and LSTM
đđ§đđŤđ đ˛ đđđŹđđ đŚđ¨đđđĽđŹ, đđ¨đŽđ§đđđđ˘đ¨đ§đŹ
- Energy based models
- LV-EBMs models
đđŹđŹđ¨đđ˘đđđ˘đŻđ đŚđđŚđ¨đŤđ˘đđŹ
- Energy based models
- Attention & transformer
đđŤđđŠđĄđŹ
- Graph transformer nets
- Graph convolutional nets
- Control
đđĽđđ§đ§đ˘đ§đ đđ§đ đđ¨đ§đđŤđ¨đĽ
- The Truck Backer-Upper
- Prediction and Planning Under Uncertainty
đđŠđđ˘đŚđ˘đłđđđ˘đ¨đ§
- Models Optimization
Course materials đ:
đđ¨đŽđŤđŹđ đ§đ¨đđđđ¨đ¨đ¤ [đđ§đ đĽđ˘đŹđĄ đŻđđŤđŹđ˘đ¨đ§]: https://lnkd.in/gpZi-adE
đđ¨đŽđŤđŹđ đ§đ¨đđđđ¨đ¨đ¤ [đ đŤđđ§đđĄ đŻđđŤđŹđ˘đ¨đ§]: https://lnkd.in/gAzgrMWx
đđ¨đŽđŤđŹđ đŻđ˘đđđ¨đŹ đĽđđđđŽđŤđđŹ: https://lnkd.in/gGnRDiR5
đđ¨đŽđŤđđ đđ¨đđ: https://lnkd.in/gwFyzqGV
đđđđđ˘đ đđ¨đŤđŚ: https://lnkd.in/gnU-3528
#deep_learning #datascience #neuralnetworks #ai #python #machine_learning #course
đ @Data_science_hub
The NYU Center for Data Science made their Deep Learning course public and shared all the class material (notebook, slides, video lectures). The class is taught by Yann LeCun, VP and Chief AI Scientist at Facebook, and Alfredo Canziani, Professor of Computer Science at New York University.
The course materials are available both in đđ§đ đĽđ˘đŹđĄ đŹđ§and đ đŤđđ§đđĄ đŤđˇ.
The course covers the following topics:
đđ§đđŤđ¨đđŽđđđ˘đ¨đ§
- history of deep learning models
- Gradient descent and the backpropagation algorithm
- Neural nets inference, architect, and training
đđđŤđđŚđđđđŤđŹ đŹđĄđđŤđ˘đ§đ
- Recurrent and convolutional nets
- ConvNets in practice
- Recurrent neural networks and LSTM
đđ§đđŤđ đ˛ đđđŹđđ đŚđ¨đđđĽđŹ, đđ¨đŽđ§đđđđ˘đ¨đ§đŹ
- Energy based models
- LV-EBMs models
đđŹđŹđ¨đđ˘đđđ˘đŻđ đŚđđŚđ¨đŤđ˘đđŹ
- Energy based models
- Attention & transformer
đđŤđđŠđĄđŹ
- Graph transformer nets
- Graph convolutional nets
- Control
đđĽđđ§đ§đ˘đ§đ đđ§đ đđ¨đ§đđŤđ¨đĽ
- The Truck Backer-Upper
- Prediction and Planning Under Uncertainty
đđŠđđ˘đŚđ˘đłđđđ˘đ¨đ§
- Models Optimization
Course materials đ:
đđ¨đŽđŤđŹđ đ§đ¨đđđđ¨đ¨đ¤ [đđ§đ đĽđ˘đŹđĄ đŻđđŤđŹđ˘đ¨đ§]: https://lnkd.in/gpZi-adE
đđ¨đŽđŤđŹđ đ§đ¨đđđđ¨đ¨đ¤ [đ đŤđđ§đđĄ đŻđđŤđŹđ˘đ¨đ§]: https://lnkd.in/gAzgrMWx
đđ¨đŽđŤđŹđ đŻđ˘đđđ¨đŹ đĽđđđđŽđŤđđŹ: https://lnkd.in/gGnRDiR5
đđ¨đŽđŤđđ đđ¨đđ: https://lnkd.in/gwFyzqGV
đđđđđ˘đ đđ¨đŤđŚ: https://lnkd.in/gnU-3528
#deep_learning #datascience #neuralnetworks #ai #python #machine_learning #course
đ @Data_science_hub