[COURSERA] NEURAL NETWORKS AND DEEP LEARNING [FCO]

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[FreeCoursesOnline.Me] Coursera - Neural Networks and Deep Learning 001.Welcome to the Deep Learning Specialization
  • 001. Welcome.mp4 (10.2 MB)
  • 001. Welcome.srt (8.8 KB)
002.Introduction to Deep Learning
  • 002. What is a neural network.mp4 (10.0 MB)
  • 002. What is a neural network.srt (9.9 KB)
  • 003. Supervised Learning with Neural Networks.mp4 (12.9 MB)
  • 003. Supervised Learning with Neural Networks.srt (11.9 KB)
  • 004. Why is Deep Learning taking off.mp4 (18.6 MB)
  • 004. Why is Deep Learning taking off.srt (17.9 KB)
  • 005. About this Course.mp4 (4.7 MB)
  • 005. About this Course.srt (4.3 KB)
  • 006. Course Resources.mp4 (2.5 MB)
  • 006. Course Resources.srt (3.6 KB)
003.Heroes of Deep Learning (Optional)
  • 007. Geoffrey Hinton interview.mp4 (191.8 MB)
  • 007. Geoffrey Hinton interview.srt (57.5 KB)
004.Logistic Regression as a Neural Network
  • 008. Binary Classification.mp4 (15.2 MB)
  • 008. Binary Classification.srt (10.6 KB)
  • 009. Logistic Regression.mp4 (8.5 MB)
  • 009. Logistic Regression.srt (7.6 KB)
  • 010. Logistic Regression Cost Function.mp4 (13.2 MB)
  • 010. Logistic Regression Cost Function.srt (11.0 KB)
  • 011. Gradient Descent.mp4 (17.0 MB)
  • 011. Gradient Descent.srt (15.4 KB)
  • 012. Derivatives.mp4 (13.4 MB)
  • 012. Derivatives.srt (12.0 KB)
  • 013. More Derivative Examples.mp4 (16.8 MB)
  • 013. More Derivative Examples.srt (12.9 KB)
  • 014. Computation graph.mp4 (5.7 MB)
  • 014. Computation graph.srt (4.3 KB)
  • 015. Derivatives with a Computation Graph.mp4 (21.7 MB)
  • 015. Derivatives with a Computation Graph.srt (16.3 KB)
  • 016. Logistic Regression Gradient Descent.mp4 (11.2 MB)
  • 016. Logistic Regression Gradient Descent.srt (9.0 KB)
  • 017. Gradient Descent on m Examples.mp4 (12.2 MB)
  • 017. Gradient Descent on m Examples.srt (12.3 KB)
005.Python and Vectorization
  • 018. Vectorization.mp4 (12.6 MB)
  • 018. Vectorization.srt (9.6 KB)
  • 019. More Vectorization Examples.mp4 (10.3 MB)
  • 019. More Vectorization Examples.srt (7.4 KB)
  • 020. Vectorizing Logistic Regression.mp4 (11.5 MB)
  • 020. Vectorizing Logistic Regression.srt (9.6 KB)
  • 021. Vectorizing Logistic Regression's Gradient Output.mp4 (15.5 MB)
  • 021. Vectorizing Logistic Regression's Gradient Output.srt (10.7 KB)
  • 022. Broadcasting in Python.mp4 (16.2 MB)
  • 022. Broadcasting in Python.srt (14.0 KB)
  • 023. A note on python numpy vectors.mp4 (12.4 MB)
  • 023. A note on python numpy vectors.srt (9.0 KB)
  • 024. Quick tour of Jupyter iPython Notebooks.mp4 (9.2 MB)
  • 024. Quick tour of Jupyter iPython Notebooks.srt (5.8 KB)
  • 025. Explanation of logistic regression cost function (optional).mp4 (10.5 MB)
  • 025. Explanation of logistic regression cost function (optional).srt (8.5 KB)
006.Heroes of Deep Learning (Optional)
  • 026. Pieter Abbeel interview.mp4 (80.0 MB)
  • 026. Pieter Abbeel interview.srt (26.9 KB)
007.Shallow Neural Network
  • 027. Neural Networks Overview.mp4 (7.2 MB)
  • 027. Neural Networks Overview.srt (6.6 KB)
  • 028. Neural Network Representation.mp4 (8.3 MB)
  • 028. Neural Network Representation.srt (8.1 KB)
  • 029. Computing a Neural Network's Output.mp4 (16.3 MB)
  • 029. Computing a Neural Network's Output.srt (16.5 KB)
  • 030. Vectorizing across multiple examples.mp4 (13.9 MB)
  • 030. Vectorizing across multiple examples.srt (10.1 KB)
  • 031. Explanation for Vectorized Implementation.mp4 (12.0 MB)
  • 031. Explanation for Vectorized Implementation.srt (8.7 KB)
  • 032. Activation functions.mp4 (19.9 MB)
  • 032. Activation functions.srt (17.0 KB)
  • 033. Why do you need non-linear activation functions.mp4 (9.3 MB)
  • 033. Why do you need non-linear activation functions.srt (7.7 KB)
  • 034. Derivatives of activation functions.mp4 (11.4 MB)
  • 034. Derivatives of activation functions.srt (11.3 KB)
  • 035. Gradient descent for Neural Networks.mp4 (16.0 MB)
  • 035. Gradient descent for Neural Networks.srt (13.4 KB)
  • 036. Backpropagation intuition (optional).mp4 (26.0 MB)
  • 036. Backpropagation intuition (optional).srt (17.7 KB)
  • 037. Random Initialization.mp4 (12.0 MB)
  • 037. Random Initialization.srt (10.4 KB)
008.Heroes of Deep Learning (Optional)
  • 038. Ian Goodfellow interview.mp4 (54.5 MB)
  • 038. Ian Goodfellow interview.srt (23.1 KB)
009.Deep Neural Network
  • 039. Deep L-layer neural network.mp4 (10.3 MB)
  • 039. Deep L-layer neural network.srt (7.4 KB)
  • 040. Forward Propagation in a Deep Network.mp4 (13.0 MB)
  • 040. Forward Propagation in a Deep Network.srt (9.9 KB)
  • 041. Getting your matrix dimensions right.mp4 (17.4 MB)
  • 041. Getting your matrix dimensions right.srt (11.4 KB)
  • 042. Why deep representations.mp4 (17.6 MB)
  • 042. Why deep representations.srt (14.5 KB)
  • 043. Building blocks of deep neural networks.mp4 (12.8 MB)
  • 043. Building blocks of deep neural networks.srt (10.9 KB)
  • 044. Forward and Backward Propagation.mp4 (19.8 MB)
  • 044. Forward and Backward Propagation.srt (13.4 KB)
  • 045. Parameters vs Hyperparameters.mp4 (10.2 MB)
  • 045. Parameters vs Hyperparameters.srt (13.0 KB)
  • 046. What does this have to do with the brain.mp4 (6.0 MB)
  • 046. What does this have to do with the brain.srt (5.6 KB)
  • [FreeCoursesOnline.Me].url (0.1 KB)
  • [FreeTutorials.Us].url (0.1 KB)
  • [FTU Forum].url (0.2 KB)

Description

[COURSERA] NEURAL NETWORKS AND DEEP LEARNING [FCO]

About this course: If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new “superpower” that will let you build AI systems that just weren’t possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: – Understand the major technology trends driving Deep Learning – Be able to build, train and apply fully connected deep neural networks – Know how to implement efficient (vectorized) neural networks – Understand the key parameters in a neural network’s architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization.

For More Udemy Free Courses >>> http://www.freetutorials.us
For more Coursera and other Courses >>> https://www.freecoursesonline.me/



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878.1 MB
seeders:20
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[COURSERA] NEURAL NETWORKS AND DEEP LEARNING [FCO]

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Download torrent
878.1 MB
seeders:20
leechers:137
[COURSERA] NEURAL NETWORKS AND DEEP LEARNING [FCO]

Torrent hash: B6CA0B67C05295BCC1A7C3B61B37E3197D2B691D