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