[Coursera] Introduction to Deep Learning

seeders: 15
leechers: 30
updated:
Download torrent
Added by CourseClub in Other > Tutorials
Download Fast Safe Anonymous
movies, software, shows...

Files

[CourseClub.NET] Coursera - Introduction to Deep Learning 001.Specialization Promo
  • 001. Welcome to AML specialization!.mp4 (13.7 MB)
  • 001. Welcome to AML specialization!.srt (4.7 KB)
002.Course intro
  • 002. Course intro.mp4 (22.1 MB)
  • 002. Course intro.srt (8.8 KB)
003.Linear model as the simplest neural network
  • 003. Linear regression.mp4 (35.7 MB)
  • 003. Linear regression.srt (13.3 KB)
  • 004. Linear classification.mp4 (42.7 MB)
  • 004. Linear classification.srt (16.4 KB)
  • 005. Gradient descent.mp4 (19.0 MB)
  • 005. Gradient descent.srt (7.4 KB)
004.Regularization in machine learning
  • 006. Overfitting problem and model validation.mp4 (26.4 MB)
  • 006. Overfitting problem and model validation.srt (9.8 KB)
  • 007. Model regularization.mp4 (19.9 MB)
  • 007. Model regularization.srt (7.4 KB)
005.Stochastic methods for optimization
  • 008. Stochastic gradient descent.mp4 (21.1 MB)
  • 008. Stochastic gradient descent.srt (7.8 KB)
  • 009. Gradient descent extensions.mp4 (36.6 MB)
  • 009. Gradient descent extensions.srt (13.4 KB)
006.The simplest neural network MLP
  • 010. Multilayer perceptron (MLP).mp4 (44.7 MB)
  • 010. Multilayer perceptron (MLP).srt (18.5 KB)
  • 011. Chain rule.mp4 (26.6 MB)
  • 011. Chain rule.srt (10.0 KB)
  • 012. Backpropagation.mp4 (31.6 MB)
  • 012. Backpropagation.srt (11.4 KB)
007.Matrix derivatives
  • 013. Efficient MLP implementation.mp4 (47.1 MB)
  • 013. Efficient MLP implementation.srt (16.6 KB)
  • 014. Other matrix derivatives.mp4 (21.4 MB)
  • 014. Other matrix derivatives.srt (8.6 KB)
008.TensorFlow framework
  • 015. What is TensorFlow.mp4 (39.4 MB)
  • 015. What is TensorFlow.srt (14.7 KB)
  • 016. Our first model in TensorFlow.mp4 (36.8 MB)
  • 016. Our first model in TensorFlow.srt (13.8 KB)
009.Philosophy of deep learning
  • 017. What Deep Learning is and is not.mp4 (29.5 MB)
  • 017. What Deep Learning is and is not.srt (13.9 KB)
  • 018. Deep learning as a language.mp4 (24.6 MB)
  • 018. Deep learning as a language.srt (11.9 KB)
010.Introduction to CNN
  • 019. Motivation for convolutional layers.mp4 (41.4 MB)
  • 019. Motivation for convolutional layers.srt (16.0 KB)
  • 020. Our first CNN architecture.mp4 (42.6 MB)
  • 020. Our first CNN architecture.srt (13.3 KB)
011.Modern CNNs
  • 021. Training tips and tricks for deep CNNs.mp4 (57.9 MB)
  • 021. Training tips and tricks for deep CNNs.srt (18.2 KB)
  • 022. Overview of modern CNN architectures.mp4 (32.2 MB)
  • 022. Overview of modern CNN architectures.srt (9.5 KB)
012.Applications of CNNs
  • 023. Learning new tasks with pre-trained CNNs.mp4 (19.3 MB)
  • 023. Learning new tasks with pre-trained CNNs.srt (6.8 KB)
  • 024. A glimpse of other Computer Vision tasks.mp4 (30.7 MB)
  • 024. A glimpse of other Computer Vision tasks.srt (10.8 KB)
013.Intro to Unsupervised Learning
  • 025. Unsupervised learning what it is and why bother.mp4 (23.8 MB)
  • 025. Unsupervised learning what it is and why bother.srt (9.5 KB)
  • 026. Autoencoders 101.mp4 (22.1 MB)
  • 026. Autoencoders 101.srt (8.1 KB)
014.More Autoencoders
  • 027. Autoencoder applications.mp4 (40.8 MB)
  • 027. Autoencoder applications.srt (14.7 KB)
  • 028. Autoencoder applications image generation, data visualization & more.mp4 (28.2 MB)
  • 028. Autoencoder applications image generation, data visualization & more.srt (10.6 KB)
015.Word Embeddings
  • 029. Natural language processing primer.mp4 (36.7 MB)
  • 029. Natural language processing primer.srt (15.3 KB)
  • 030. Word embeddings.mp4 (48.3 MB)
  • 030. Word embeddings.srt (20.2 KB)
016.Generative Adversarial Networks
  • 031. Generative models 101.mp4 (26.7 MB)
  • 031. Generative models 101.srt (11.2 KB)
  • 032. Generative Adversarial Networks.mp4 (36.2 MB)
  • 032. Generative Adversarial Networks.srt (15.3 KB)
  • 033. Applications of adversarial approach.mp4 (41.9 MB)
  • 033. Applications of adversarial approach.srt (15.9 KB)
017.Introduction to RNN
  • 034. Motivation for recurrent layers.mp4 (30.2 MB)
  • 034. Motivation for recurrent layers.srt (10.6 KB)
  • 035. Simple RNN and Backpropagation.mp4 (35.1 MB)
  • 035. Simple RNN and Backpropagation.srt (12.5 KB)
018.Modern RNNs
  • 036. The training of RNNs is not that easy.mp4 (26.4 MB)
  • 036. The training of RNNs is not that easy.srt (10.4 KB)
  • 037. Dealing with vanishing and exploding gradients.mp4 (34.9 MB)
  • 037. Dealing with vanishing and exploding gradients.srt (13.7 KB)
  • 038. Modern RNNs LSTM and GRU.mp4 (47.7 MB)
  • 038. Modern RNNs LSTM and GRU.srt (17.2 KB)
019.Applications of RNNs
  • 039. Practical use cases for RNNs.mp4 (56.1 MB)
  • 039. Practical use cases for RNNs.srt (19.5 KB)
  • [CourseClub.NET].url (0.1 KB)
  • [FCS Forum].url (0.1 KB)
  • [FreeCourseSite.com].url (0.1 KB)

Description

[Coursera] Introduction to Deep Learning

The goal of this course is to give learners basic understanding of modern neural networks.

For More Courses: https://courseclub.net



Download torrent
1.3 GB
seeders:15
leechers:30
[Coursera] Introduction to Deep Learning

Trackers

tracker name
udp://62.138.0.158:6969/announce
udp://87.233.192.220:6969/announce
udp://88.198.231.1:1337/announce
udp://151.80.120.113:2710/announce
udp://111.6.78.96:6969/announce
udp://90.179.64.91:1337/announce
udp://51.15.4.13:1337/announce
udp://191.96.249.23:6969/announce
udp://35.187.36.248:1337/announce
udp://123.249.16.65:2710/announce
udp://127.0.0.1:6969/announce
udp://210.244.71.25:6969/announce
udp://78.142.19.42:1337/announce
udp://173.254.219.72:6969/announce
udp://51.15.76.199:6969/announce
udp://91.212.150.191:3418/announce
udp://103.224.212.222:6969/announce
udp://92.241.171.245:6969/announce
udp://51.15.40.114:80/announce
udp://37.19.5.139:6969/announce
µTorrent compatible trackers list

Download torrent
1.3 GB
seeders:15
leechers:30
[Coursera] Introduction to Deep Learning

Torrent hash: 70E9796992BCBB00F65F3672AE86EB335593F36E