|
Обратите внимание, что наш сайт не размещает какие-либо файлы из списка. Вы не можете скачать
эти файлы или скачать torrent-файл.
|
| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 0 |
66б |
| 1 |
260б |
| 1. Artificial Neural Networks Section Introduction.mp4 |
29.82Мб |
| 1. Artificial Neural Networks Section Introduction.srt |
7.90Кб |
| 1. Beginner's Coding Tips.mp4 |
75.71Мб |
| 1. Beginner's Coding Tips.srt |
19.02Кб |
| 1. Deep Reinforcement Learning Section Introduction.mp4 |
38.05Мб |
| 1. Deep Reinforcement Learning Section Introduction.srt |
8.60Кб |
| 1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4 |
38.68Мб |
| 1. Differences Between Tensorflow 1.x and Tensorflow 2.x.srt |
12.20Кб |
| 1. Embeddings.mp4 |
52.56Мб |
| 1. Embeddings.srt |
16.20Кб |
| 1. GAN Theory.mp4 |
87.16Мб |
| 1. GAN Theory.srt |
20.71Кб |
| 1. Gradient Descent.mp4 |
34.92Мб |
| 1. Gradient Descent.srt |
9.77Кб |
| 1. How to Choose Hyperparameters.mp4 |
37.92Мб |
| 1. How to Choose Hyperparameters.srt |
8.71Кб |
| 1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 |
150.59Мб |
| 1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt |
14.69Кб |
| 1. How to Succeed in this Course (Long Version).mp4 |
35.22Мб |
| 1. How to Succeed in this Course (Long Version).srt |
14.61Кб |
| 1. Introduction.mp4 |
34.81Мб |
| 1. Introduction.srt |
5.70Кб |
| 1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 |
53.84Мб |
| 1. Intro to Google Colab, how to use a GPU or TPU for free.srt |
14.13Кб |
| 1. Mean Squared Error.mp4 |
33.77Мб |
| 1. Mean Squared Error.srt |
11.21Кб |
| 1. Recommender Systems with Deep Learning Theory.mp4 |
68.66Мб |
| 1. Recommender Systems with Deep Learning Theory.srt |
17.40Кб |
| 1. Reinforcement Learning Stock Trader Introduction.mp4 |
26.04Мб |
| 1. Reinforcement Learning Stock Trader Introduction.srt |
6.84Кб |
| 1. Sequence Data.mp4 |
90.15Мб |
| 1. Sequence Data.srt |
24.02Кб |
| 1. Transfer Learning Theory.mp4 |
55.13Мб |
| 1. Transfer Learning Theory.srt |
10.66Кб |
| 1. What is a Web Service (Tensorflow Serving pt 1).mp4 |
27.78Мб |
| 1. What is a Web Service (Tensorflow Serving pt 1).srt |
7.71Кб |
| 1. What is Convolution (part 1).mp4 |
79.77Мб |
| 1. What is Convolution (part 1).srt |
20.16Кб |
| 1. What is Machine Learning.mp4 |
65.50Мб |
| 1. What is Machine Learning.srt |
18.45Кб |
| 1. What is the Appendix.mp4 |
16.38Мб |
| 1. What is the Appendix.srt |
3.75Кб |
| 10 |
1023.65Кб |
| 10. ANN for Regression.mp4 |
69.27Мб |
| 10. ANN for Regression.srt |
12.78Кб |
| 10. Batch Normalization.mp4 |
21.11Мб |
| 10. Batch Normalization.srt |
6.53Кб |
| 10. Epsilon-Greedy.mp4 |
40.11Мб |
| 10. Epsilon-Greedy.srt |
7.49Кб |
| 10. GRU and LSTM (pt 2).mp4 |
50.36Мб |
| 10. GRU and LSTM (pt 2).srt |
14.28Кб |
| 10. Help! Why is the code slower on my machine.mp4 |
42.46Мб |
| 10. Help! Why is the code slower on my machine.srt |
11.72Кб |
| 10. Why Keras.mp4 |
26.51Мб |
| 10. Why Keras.srt |
5.77Кб |
| 100 |
763.80Кб |
| 101 |
119.38Кб |
| 102 |
231.53Кб |
| 103 |
26.77Кб |
| 104 |
292.89Кб |
| 105 |
305.20Кб |
| 106 |
438.91Кб |
| 107 |
186.10Кб |
| 108 |
280.15Кб |
| 109 |
312.70Кб |
| 11 |
430.21Кб |
| 11. A More Challenging Sequence.mp4 |
64.65Мб |
| 11. A More Challenging Sequence.srt |
9.60Кб |
| 11. Improving CIFAR-10 Results.mp4 |
72.91Мб |
| 11. Improving CIFAR-10 Results.srt |
13.17Кб |
| 11. Q-Learning.mp4 |
61.83Мб |
| 11. Q-Learning.srt |
17.91Кб |
| 11. Suggestion Box.mp4 |
27.12Мб |
| 11. Suggestion Box.srt |
4.75Кб |
| 110 |
901.13Кб |
| 111 |
241.71Кб |
| 112 |
683.72Кб |
| 113 |
229.97Кб |
| 114 |
373.09Кб |
| 115 |
730.25Кб |
| 116 |
899.63Кб |
| 117 |
497.62Кб |
| 118 |
979.29Кб |
| 119 |
19.09Кб |
| 12 |
474.85Кб |
| 12. Deep Q-Learning DQN (pt 1).mp4 |
56.27Мб |
| 12. Deep Q-Learning DQN (pt 1).srt |
16.43Кб |
| 12. Demo of the Long Distance Problem.mp4 |
124.05Мб |
| 12. Demo of the Long Distance Problem.srt |
23.06Кб |
| 120 |
23.79Кб |
| 121 |
985.73Кб |
| 122 |
325.09Кб |
| 123 |
714.83Кб |
| 124 |
32.91Кб |
| 125 |
750.86Кб |
| 126 |
887.27Кб |
| 127 |
913.42Кб |
| 128 |
433.49Кб |
| 129 |
587.24Кб |
| 13 |
144.86Кб |
| 13. Deep Q-Learning DQN (pt 2).mp4 |
49.60Мб |
| 13. Deep Q-Learning DQN (pt 2).srt |
13.21Кб |
| 13. RNN for Image Classification (Theory).mp4 |
29.12Мб |
| 13. RNN for Image Classification (Theory).srt |
5.99Кб |
| 130 |
416.94Кб |
| 131 |
639.12Кб |
| 14 |
236.64Кб |
| 14. How to Learn Reinforcement Learning.mp4 |
37.70Мб |
| 14. How to Learn Reinforcement Learning.srt |
7.62Кб |
| 14. RNN for Image Classification (Code).mp4 |
23.30Мб |
| 14. RNN for Image Classification (Code).srt |
4.19Кб |
| 15 |
298.03Кб |
| 15. Stock Return Predictions using LSTMs (pt 1).mp4 |
67.11Мб |
| 15. Stock Return Predictions using LSTMs (pt 1).srt |
15.71Кб |
| 16 |
721.01Кб |
| 16. Stock Return Predictions using LSTMs (pt 2).mp4 |
32.97Мб |
| 16. Stock Return Predictions using LSTMs (pt 2).srt |
6.50Кб |
| 17 |
123.49Кб |
| 17. Stock Return Predictions using LSTMs (pt 3).mp4 |
67.34Мб |
| 17. Stock Return Predictions using LSTMs (pt 3).srt |
14.42Кб |
| 18 |
301.93Кб |
| 18. Other Ways to Forecast.mp4 |
28.33Мб |
| 18. Other Ways to Forecast.srt |
7.18Кб |
| 19 |
952.59Кб |
| 2 |
245б |
| 2. Anaconda Environment Setup.mp4 |
180.90Мб |
| 2. Anaconda Environment Setup.srt |
19.96Кб |
| 2. Beginners Rejoice The Math in This Course is Optional.mp4 |
68.52Мб |
| 2. Beginners Rejoice The Math in This Course is Optional.srt |
17.02Кб |
| 2. Binary Cross Entropy.mp4 |
23.68Мб |
| 2. Binary Cross Entropy.srt |
7.26Кб |
| 2. BONUS Lecture.mp4 |
37.79Мб |
| 2. BONUS Lecture.srt |
7.87Кб |
| 2. Code Preparation (Classification Theory).mp4 |
59.80Мб |
| 2. Code Preparation (Classification Theory).srt |
20.26Кб |
| 2. Code Preparation (NLP).mp4 |
57.04Мб |
| 2. Code Preparation (NLP).srt |
16.82Кб |
| 2. Constants and Basic Computation.mp4 |
40.30Мб |
| 2. Constants and Basic Computation.srt |
9.63Кб |
| 2. Data and Environment.mp4 |
50.97Мб |
| 2. Data and Environment.srt |
15.69Кб |
| 2. Elements of a Reinforcement Learning Problem.mp4 |
98.59Мб |
| 2. Elements of a Reinforcement Learning Problem.srt |
26.19Кб |
| 2. Forecasting.mp4 |
46.75Мб |
| 2. Forecasting.srt |
13.35Кб |
| 2. GAN Code.mp4 |
78.30Мб |
| 2. GAN Code.srt |
14.88Кб |
| 2. How to Code Yourself (part 1).mp4 |
71.85Мб |
| 2. How to Code Yourself (part 1).srt |
22.13Кб |
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 |
105.61Мб |
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt |
31.63Кб |
| 2. Outline.mp4 |
73.67Мб |
| 2. Outline.srt |
17.10Кб |
| 2. Recommender Systems with Deep Learning Code.mp4 |
58.81Мб |
| 2. Recommender Systems with Deep Learning Code.srt |
11.70Кб |
| 2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 |
31.57Мб |
| 2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt |
7.29Кб |
| 2. Stochastic Gradient Descent.mp4 |
22.97Мб |
| 2. Stochastic Gradient Descent.srt |
5.40Кб |
| 2. Tensorflow 2.0 in Google Colab.mp4 |
40.65Мб |
| 2. Tensorflow 2.0 in Google Colab.srt |
9.48Кб |
| 2. Tensorflow Serving pt 2.mp4 |
104.99Мб |
| 2. Tensorflow Serving pt 2.srt |
20.42Кб |
| 2. What is Convolution (part 2).mp4 |
22.27Мб |
| 2. What is Convolution (part 2).srt |
7.25Кб |
| 2. Where Are The Exercises.mp4 |
25.98Мб |
| 2. Where Are The Exercises.srt |
5.41Кб |
| 20 |
335.23Кб |
| 21 |
420.79Кб |
| 22 |
91.99Кб |
| 23 |
154.55Кб |
| 24 |
309.91Кб |
| 25 |
554.63Кб |
| 26 |
567.77Кб |
| 27 |
578.01Кб |
| 28 |
742.93Кб |
| 29 |
353.06Кб |
| 3 |
545.40Кб |
| 3.1 Colab Notebooks.html |
157б |
| 3.2 Github Link.html |
120б |
| 3. Autoregressive Linear Model for Time Series Prediction.mp4 |
71.70Мб |
| 3. Autoregressive Linear Model for Time Series Prediction.srt |
14.23Кб |
| 3. Categorical Cross Entropy.mp4 |
31.70Мб |
| 3. Categorical Cross Entropy.srt |
9.62Кб |
| 3. Classification Notebook.mp4 |
54.54Мб |
| 3. Classification Notebook.srt |
9.40Кб |
| 3. Forward Propagation.mp4 |
46.70Мб |
| 3. Forward Propagation.srt |
12.20Кб |
| 3. How to Code Yourself (part 2).mp4 |
49.14Мб |
| 3. How to Code Yourself (part 2).srt |
12.98Кб |
| 3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 |
167.30Мб |
| 3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt |
32.01Кб |
| 3. Large Datasets and Data Generators.mp4 |
36.56Мб |
| 3. Large Datasets and Data Generators.srt |
8.80Кб |
| 3. Links to TF2.0 Notebooks.html |
8.11Кб |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 |
79.71Мб |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt |
16.11Кб |
| 3. Momentum.mp4 |
34.25Мб |
| 3. Momentum.srt |
7.84Кб |
| 3. Replay Buffer.mp4 |
24.04Мб |
| 3. Replay Buffer.srt |
6.94Кб |
| 3. States, Actions, Rewards, Policies.mp4 |
43.33Мб |
| 3. States, Actions, Rewards, Policies.srt |
11.32Кб |
| 3. Tensorflow Lite (TFLite).mp4 |
42.59Мб |
| 3. Tensorflow Lite (TFLite).srt |
11.03Кб |
| 3. Text Preprocessing.mp4 |
28.76Мб |
| 3. Text Preprocessing.srt |
6.15Кб |
| 3. Uploading your own data to Google Colab.mp4 |
73.59Мб |
| 3. Uploading your own data to Google Colab.srt |
11.98Кб |
| 3. Variables and Gradient Tape.mp4 |
56.05Мб |
| 3. Variables and Gradient Tape.srt |
13.58Кб |
| 3. What is Convolution (part 3).mp4 |
27.64Мб |
| 3. What is Convolution (part 3).srt |
8.01Кб |
| 3. Where to get the code.mp4 |
62.91Мб |
| 3. Where to get the code.srt |
15.36Кб |
| 30 |
492.17Кб |
| 31 |
3.19Кб |
| 32 |
673.47Кб |
| 33 |
909.01Кб |
| 34 |
488.94Кб |
| 35 |
512.71Кб |
| 36 |
362.37Кб |
| 37 |
95.66Кб |
| 38 |
172.05Кб |
| 39 |
208.31Кб |
| 4 |
852.44Кб |
| 4. 2 Approaches to Transfer Learning.mp4 |
20.58Мб |
| 4. 2 Approaches to Transfer Learning.srt |
5.96Кб |
| 4. Build Your Own Custom Model.mp4 |
58.55Мб |
| 4. Build Your Own Custom Model.srt |
13.28Кб |
| 4. Code Preparation (Regression Theory).mp4 |
27.29Мб |
| 4. Code Preparation (Regression Theory).srt |
9.07Кб |
| 4. Convolution on Color Images.mp4 |
69.44Мб |
| 4. Convolution on Color Images.srt |
20.56Кб |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 |
108.17Мб |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt |
23.01Кб |
| 4. Markov Decision Processes (MDPs).mp4 |
49.35Мб |
| 4. Markov Decision Processes (MDPs).srt |
12.65Кб |
| 4. Program Design and Layout.mp4 |
25.98Мб |
| 4. Program Design and Layout.srt |
8.64Кб |
| 4. Proof that the Linear Model Works.mp4 |
16.20Мб |
| 4. Proof that the Linear Model Works.srt |
4.56Кб |
| 4. Proof that using Jupyter Notebook is the same as not using it.mp4 |
69.45Мб |
| 4. Proof that using Jupyter Notebook is the same as not using it.srt |
14.22Кб |
| 4. Text Classification with LSTMs.mp4 |
50.68Мб |
| 4. Text Classification with LSTMs.srt |
9.80Кб |
| 4. The Geometrical Picture.mp4 |
56.43Мб |
| 4. The Geometrical Picture.srt |
11.51Кб |
| 4. Variable and Adaptive Learning Rates.mp4 |
34.85Мб |
| 4. Variable and Adaptive Learning Rates.srt |
15.15Кб |
| 4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 |
38.93Мб |
| 4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt |
11.53Кб |
| 4. Why is Google the King of Distributed Computing.mp4 |
44.93Мб |
| 4. Why is Google the King of Distributed Computing.srt |
11.25Кб |
| 40 |
197.48Кб |
| 41 |
465.07Кб |
| 42 |
547.13Кб |
| 43 |
982.01Кб |
| 44 |
582.80Кб |
| 45 |
745.81Кб |
| 46 |
977.70Кб |
| 47 |
895.29Кб |
| 48 |
906.04Кб |
| 49 |
472.70Кб |
| 5 |
403.99Кб |
| 5. Activation Functions.mp4 |
80.54Мб |
| 5. Activation Functions.srt |
22.64Кб |
| 5. Adam (pt 1).mp4 |
55.12Мб |
| 5. Adam (pt 1).srt |
16.67Кб |
| 5. CNN Architecture.mp4 |
80.58Мб |
| 5. CNN Architecture.srt |
27.89Кб |
| 5. CNNs for Text.mp4 |
40.40Мб |
| 5. CNNs for Text.srt |
10.09Кб |
| 5. Code pt 1.mp4 |
39.55Мб |
| 5. Code pt 1.srt |
7.21Кб |
| 5. How to Succeed in this Course.mp4 |
43.75Мб |
| 5. How to Succeed in this Course.srt |
8.28Кб |
| 5. Is Theano Dead.mp4 |
40.76Мб |
| 5. Is Theano Dead.srt |
12.63Кб |
| 5. Recurrent Neural Networks.mp4 |
83.00Мб |
| 5. Recurrent Neural Networks.srt |
25.59Кб |
| 5. Regression Notebook.mp4 |
57.47Мб |
| 5. Regression Notebook.srt |
12.13Кб |
| 5. The Return.mp4 |
21.13Мб |
| 5. The Return.srt |
6.26Кб |
| 5. Training with Distributed Strategies.mp4 |
43.54Мб |
| 5. Training with Distributed Strategies.srt |
8.53Кб |
| 5. Transfer Learning Code (pt 1).mp4 |
66.52Мб |
| 5. Transfer Learning Code (pt 1).srt |
13.76Кб |
| 50 |
165.18Кб |
| 51 |
92.57Кб |
| 52 |
241.16Кб |
| 53 |
450.47Кб |
| 54 |
498.10Кб |
| 55 |
534.88Кб |
| 56 |
975.51Кб |
| 57 |
28.25Кб |
| 58 |
82.61Кб |
| 59 |
329.98Кб |
| 6 |
8.50Кб |
| 6. Adam (pt 2).mp4 |
52.76Мб |
| 6. Adam (pt 2).srt |
14.48Кб |
| 6. CNN Code Preparation.mp4 |
76.88Мб |
| 6. CNN Code Preparation.srt |
19.65Кб |
| 6. Code pt 2.mp4 |
68.00Мб |
| 6. Code pt 2.srt |
11.75Кб |
| 6. Multiclass Classification.mp4 |
41.38Мб |
| 6. Multiclass Classification.srt |
10.98Кб |
| 6. RNN Code Preparation.mp4 |
18.43Мб |
| 6. RNN Code Preparation.srt |
7.14Кб |
| 6. Text Classification with CNNs.mp4 |
39.62Мб |
| 6. Text Classification with CNNs.srt |
6.63Кб |
| 6. The Neuron.mp4 |
42.57Мб |
| 6. The Neuron.srt |
12.46Кб |
| 6. Transfer Learning Code (pt 2).mp4 |
46.05Мб |
| 6. Transfer Learning Code (pt 2).srt |
10.43Кб |
| 6. Using the TPU.mp4 |
45.24Мб |
| 6. Using the TPU.srt |
6.96Кб |
| 6. Value Functions and the Bellman Equation.mp4 |
43.56Мб |
| 6. Value Functions and the Bellman Equation.srt |
12.51Кб |
| 60 |
659.72Кб |
| 61 |
407.58Кб |
| 62 |
666.07Кб |
| 63 |
877.25Кб |
| 64 |
48.82Кб |
| 65 |
292.23Кб |
| 66 |
251.99Кб |
| 67 |
310.46Кб |
| 68 |
968.86Кб |
| 69 |
781.20Кб |
| 7 |
416.76Кб |
| 7. CNN for Fashion MNIST.mp4 |
42.79Мб |
| 7. CNN for Fashion MNIST.srt |
7.99Кб |
| 7. Code pt 3.mp4 |
52.05Мб |
| 7. Code pt 3.srt |
7.75Кб |
| 7. How does a model learn.mp4 |
47.95Мб |
| 7. How does a model learn.srt |
14.00Кб |
| 7. How to Represent Images.mp4 |
70.46Мб |
| 7. How to Represent Images.srt |
15.60Кб |
| 7. RNN for Time Series Prediction.mp4 |
74.07Мб |
| 7. RNN for Time Series Prediction.srt |
11.21Кб |
| 7. What does it mean to “learn”.mp4 |
31.71Мб |
| 7. What does it mean to “learn”.srt |
8.92Кб |
| 70 |
74.80Кб |
| 71 |
253.83Кб |
| 72 |
451.70Кб |
| 73 |
469.10Кб |
| 74 |
688.29Кб |
| 75 |
214.27Кб |
| 76 |
262.15Кб |
| 77 |
419.43Кб |
| 78 |
442.69Кб |
| 79 |
552.37Кб |
| 8 |
873.98Кб |
| 8. CNN for CIFAR-10.mp4 |
29.69Мб |
| 8. CNN for CIFAR-10.srt |
5.38Кб |
| 8. Code Preparation (ANN).mp4 |
50.92Мб |
| 8. Code Preparation (ANN).srt |
16.30Кб |
| 8. Code pt 4.mp4 |
52.51Мб |
| 8. Code pt 4.srt |
8.37Кб |
| 8. Making Predictions.mp4 |
33.88Мб |
| 8. Making Predictions.srt |
7.99Кб |
| 8. Paying Attention to Shapes.mp4 |
52.48Мб |
| 8. Paying Attention to Shapes.srt |
9.88Кб |
| 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 |
42.74Мб |
| 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt |
12.41Кб |
| 80 |
632.44Кб |
| 81 |
247.46Кб |
| 82 |
354.32Кб |
| 83 |
613.22Кб |
| 84 |
720.29Кб |
| 85 |
914.24Кб |
| 86 |
385.15Кб |
| 87 |
462.87Кб |
| 88 |
72.99Кб |
| 89 |
322.64Кб |
| 9 |
858.23Кб |
| 9. ANN for Image Classification.mp4 |
47.71Мб |
| 9. ANN for Image Classification.srt |
9.93Кб |
| 9. Data Augmentation.mp4 |
34.95Мб |
| 9. Data Augmentation.srt |
11.24Кб |
| 9. GRU and LSTM (pt 1).mp4 |
79.86Мб |
| 9. GRU and LSTM (pt 1).srt |
22.80Кб |
| 9. Reinforcement Learning Stock Trader Discussion.mp4 |
16.59Мб |
| 9. Reinforcement Learning Stock Trader Discussion.srt |
4.39Кб |
| 9. Saving and Loading a Model.mp4 |
29.73Мб |
| 9. Saving and Loading a Model.srt |
4.93Кб |
| 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 |
52.91Мб |
| 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt |
14.88Кб |
| 90 |
969.66Кб |
| 91 |
78.95Кб |
| 92 |
212.72Кб |
| 93 |
303.98Кб |
| 94 |
446.44Кб |
| 95 |
801.07Кб |
| 96 |
50.05Кб |
| 97 |
82.45Кб |
| 98 |
153.54Кб |
| 99 |
192.06Кб |
| TutsNode.com.txt |
63б |