|
Обратите внимание, что наш сайт не размещает какие-либо файлы из списка. Вы не можете скачать
эти файлы или скачать torrent-файл.
|
| [FreeCoursesOnline.Me].url |
133б |
| [FreeTutorials.Us].url |
119б |
| [FTU Forum].url |
252б |
| 001. Welcome.mp4 |
10.21Мб |
| 001. Welcome.srt |
8.83Кб |
| 002. What is a neural network.mp4 |
9.97Мб |
| 002. What is a neural network.srt |
9.86Кб |
| 003. Supervised Learning with Neural Networks.mp4 |
12.90Мб |
| 003. Supervised Learning with Neural Networks.srt |
11.90Кб |
| 004. Why is Deep Learning taking off.mp4 |
18.64Мб |
| 004. Why is Deep Learning taking off.srt |
17.85Кб |
| 005. About this Course.mp4 |
4.66Мб |
| 005. About this Course.srt |
4.35Кб |
| 006. Course Resources.mp4 |
2.50Мб |
| 006. Course Resources.srt |
3.63Кб |
| 007. Geoffrey Hinton interview.mp4 |
191.76Мб |
| 007. Geoffrey Hinton interview.srt |
57.50Кб |
| 008. Binary Classification.mp4 |
15.24Мб |
| 008. Binary Classification.srt |
10.57Кб |
| 009. Logistic Regression.mp4 |
8.48Мб |
| 009. Logistic Regression.srt |
7.56Кб |
| 010. Logistic Regression Cost Function.mp4 |
13.19Мб |
| 010. Logistic Regression Cost Function.srt |
11.00Кб |
| 011. Gradient Descent.mp4 |
17.05Мб |
| 011. Gradient Descent.srt |
15.36Кб |
| 012. Derivatives.mp4 |
13.41Мб |
| 012. Derivatives.srt |
12.01Кб |
| 013. More Derivative Examples.mp4 |
16.76Мб |
| 013. More Derivative Examples.srt |
12.87Кб |
| 014. Computation graph.mp4 |
5.66Мб |
| 014. Computation graph.srt |
4.29Кб |
| 015. Derivatives with a Computation Graph.mp4 |
21.69Мб |
| 015. Derivatives with a Computation Graph.srt |
16.31Кб |
| 016. Logistic Regression Gradient Descent.mp4 |
11.15Мб |
| 016. Logistic Regression Gradient Descent.srt |
8.97Кб |
| 017. Gradient Descent on m Examples.mp4 |
12.17Мб |
| 017. Gradient Descent on m Examples.srt |
12.29Кб |
| 018. Vectorization.mp4 |
12.60Мб |
| 018. Vectorization.srt |
9.62Кб |
| 019. More Vectorization Examples.mp4 |
10.34Мб |
| 019. More Vectorization Examples.srt |
7.39Кб |
| 020. Vectorizing Logistic Regression.mp4 |
11.46Мб |
| 020. Vectorizing Logistic Regression.srt |
9.59Кб |
| 021. Vectorizing Logistic Regression's Gradient Output.mp4 |
15.55Мб |
| 021. Vectorizing Logistic Regression's Gradient Output.srt |
10.74Кб |
| 022. Broadcasting in Python.mp4 |
16.17Мб |
| 022. Broadcasting in Python.srt |
14.00Кб |
| 023. A note on python numpy vectors.mp4 |
12.36Мб |
| 023. A note on python numpy vectors.srt |
9.04Кб |
| 024. Quick tour of Jupyter iPython Notebooks.mp4 |
9.23Мб |
| 024. Quick tour of Jupyter iPython Notebooks.srt |
5.78Кб |
| 025. Explanation of logistic regression cost function (optional).mp4 |
10.47Мб |
| 025. Explanation of logistic regression cost function (optional).srt |
8.50Кб |
| 026. Pieter Abbeel interview.mp4 |
80.04Мб |
| 026. Pieter Abbeel interview.srt |
26.86Кб |
| 027. Neural Networks Overview.mp4 |
7.23Мб |
| 027. Neural Networks Overview.srt |
6.61Кб |
| 028. Neural Network Representation.mp4 |
8.26Мб |
| 028. Neural Network Representation.srt |
8.09Кб |
| 029. Computing a Neural Network's Output.mp4 |
16.32Мб |
| 029. Computing a Neural Network's Output.srt |
16.52Кб |
| 030. Vectorizing across multiple examples.mp4 |
13.86Мб |
| 030. Vectorizing across multiple examples.srt |
10.06Кб |
| 031. Explanation for Vectorized Implementation.mp4 |
11.97Мб |
| 031. Explanation for Vectorized Implementation.srt |
8.67Кб |
| 032. Activation functions.mp4 |
19.93Мб |
| 032. Activation functions.srt |
17.03Кб |
| 033. Why do you need non-linear activation functions.mp4 |
9.29Мб |
| 033. Why do you need non-linear activation functions.srt |
7.74Кб |
| 034. Derivatives of activation functions.mp4 |
11.38Мб |
| 034. Derivatives of activation functions.srt |
11.29Кб |
| 035. Gradient descent for Neural Networks.mp4 |
16.01Мб |
| 035. Gradient descent for Neural Networks.srt |
13.44Кб |
| 036. Backpropagation intuition (optional).mp4 |
26.04Мб |
| 036. Backpropagation intuition (optional).srt |
17.72Кб |
| 037. Random Initialization.mp4 |
11.96Мб |
| 037. Random Initialization.srt |
10.39Кб |
| 038. Ian Goodfellow interview.mp4 |
54.53Мб |
| 038. Ian Goodfellow interview.srt |
23.08Кб |
| 039. Deep L-layer neural network.mp4 |
10.35Мб |
| 039. Deep L-layer neural network.srt |
7.40Кб |
| 040. Forward Propagation in a Deep Network.mp4 |
13.02Мб |
| 040. Forward Propagation in a Deep Network.srt |
9.89Кб |
| 041. Getting your matrix dimensions right.mp4 |
17.35Мб |
| 041. Getting your matrix dimensions right.srt |
11.43Кб |
| 042. Why deep representations.mp4 |
17.59Мб |
| 042. Why deep representations.srt |
14.53Кб |
| 043. Building blocks of deep neural networks.mp4 |
12.81Мб |
| 043. Building blocks of deep neural networks.srt |
10.91Кб |
| 044. Forward and Backward Propagation.mp4 |
19.80Мб |
| 044. Forward and Backward Propagation.srt |
13.43Кб |
| 045. Parameters vs Hyperparameters.mp4 |
10.21Мб |
| 045. Parameters vs Hyperparameters.srt |
13.00Кб |
| 046. What does this have to do with the brain.mp4 |
6.00Мб |
| 046. What does this have to do with the brain.srt |
5.64Кб |