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- 收录时间:2017-02-19 文档个数:2 文档大小:1.8 GB 最近下载:2025-05-15 人气:1455 磁力链接
NANGOKU.ISO 1.8 GB
NANGOKU.MDS 4.3 kB
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7. Building a CNN/1.1 Section 40 - Convolutional Neural Networks (CNN).zip 234.9 MB
7. Building a CNN/8. Building a CNN - FINAL DEMO!.mp4 160.2 MB
25. Logistic Regression Implementation/8. Logistic Regression - Step 7.mp4 124.4 MB
7. Building a CNN/4. Building a CNN - Step 3.mp4 124.4 MB
4. Building an ANN/5. Building an ANN - Step 2.mp4 116.4 MB
7. Building a CNN/3. Building a CNN - Step 2.mp4 112.1 MB
24. Data Preprocessing Template/8. Data Preprocessing - Step 7.mp4 106.7 MB
4. Building an ANN/8. Building an ANN - Step 5.mp4 106.2 MB
7. Building a CNN/6. Building a CNN - Step 5.mp4 102.5 MB
24. Data Preprocessing Template/6. Data Preprocessing - Step 5.mp4 93.0 MB
25. Logistic Regression Implementation/3. Logistic Regression - Step 2.mp4 88.8 MB
4. Building an ANN/6. Building an ANN - Step 3.mp4 78.7 MB
24. Data Preprocessing Template/4. Data Preprocessing - Step 3.srt 75.3 MB
24. Data Preprocessing Template/4. Data Preprocessing - Step 3.mp4 75.3 MB
7. Building a CNN/2. Building a CNN - Step 1.mp4 74.2 MB
24. Data Preprocessing Template/5. Data Preprocessing - Step 4.mp4 72.4 MB
24. Data Preprocessing Template/7. Data Preprocessing - Step 6.mp4 70.9 MB
4. Building an ANN/3. Building an ANN - Step 1.mp4 69.7 MB
4. Building an ANN/7. Building an ANN - Step 4.mp4 68.5 MB
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covers/03.jpg 5.0 MB
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Exercise Files/code_33942.zip 5.1 GB
1.Deep Learning Frameworks/02.Understanding TensorFlow, Keras and PyTorch Framework.mp4 24.2 MB
1.Deep Learning Frameworks/03.Deep Learning Using CNTK and Gluon Framework.mp4 9.9 MB
2. Feed-Forward Neural Networks/04.Implementing Single and Multi-Layer Neural Network.mp4 28.3 MB
2. Feed-Forward Neural Networks/05.Experiment with Activation Functions, Hidden Layers, and Hidden Units.mp4 26.3 MB
2. Feed-Forward Neural Networks/06.Autoencoder, Loss Function, and Optimizers.mp4 27.5 MB
2. Feed-Forward Neural Networks/07.Overfitting Prevention Methods.mp4 16.5 MB
3.Convolutional and Recurrent Neural Networks/08.Optimization Techniques for CNNs.mp4 20.1 MB
3.Convolutional and Recurrent Neural Networks/09.Experimenting with Different Types of Initialization.mp4 29.8 MB
3.Convolutional and Recurrent Neural Networks/10.Implementing Simple RNN and LSTM.mp4 17.8 MB
3.Convolutional and Recurrent Neural Networks/11.Implementing GRUs and Bidirectional RNNs.mp4 17.0 MB
4.GANs and Computer Vision/12.Implementing Generative Adversarial Networks.mp4 15.5 MB
4.GANs and Computer Vision/13.Computer Vision Techniques.mp4 22.6 MB
4.GANs and Computer Vision/14.Detecting Facial Key Points and Transferring Styles.mp4 12.5 MB
5.Neural Network Learning and Data Processing/15.Hyper Parameter Selection and Tuning.mp4 19.3 MB
5.Neural Network Learning and Data Processing/16.Speech Recognition.mp4 24.1 MB
5.Neural Network Learning and Data Processing/17.Time Series and Structured Data.mp4 30.3 MB
6.Network Internals and Pretrained Models/18.Visualizing and Analysing Network.mp4 16.4 MB
6.Network Internals and Pretrained Models/19.Freezing and Storing the Network.mp4 11.4 MB
6.Network Internals and Pretrained Models/20.Using InceptionV3 and ResNet50 Model.mp4 11.7 MB
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tenoke-artifact.seeker.the.shadow.of.deep.sea.iso 1.4 GB
tenoke-artifact.seeker.the.shadow.of.deep.sea.nfo 32.9 kB
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IGG-GAMES.COM.url 196 Bytes
OneHack.Us.txt 133 Bytes
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Exercise Files/code_33630.zip 103.9 MB
1.Getting Ready with PyTorch/02.Using PyTorch.mp4 40.7 MB
1.Getting Ready with PyTorch/03.Understanding Regression.mp4 13.0 MB
1.Getting Ready with PyTorch/04.Linear Regression and Logistic Regression.mp4 34.5 MB
2.Convolutional Neural Network/05.Understanding Convolutional Neural Network.mp4 17.9 MB
2.Convolutional Neural Network/06.Looking into Images from a Machine Perspective.mp4 23.4 MB
2.Convolutional Neural Network/07.Making CNN.mp4 33.7 MB
2.Convolutional Neural Network/08.Pooling Layers.mp4 19.5 MB
2.Convolutional Neural Network/09.Output Layer.mp4 21.5 MB
3.Understanding RNN and LSTM/10.Understanding Recurrent Neural Network.mp4 29.4 MB
3.Understanding RNN and LSTM/11.Making RNN for Prediction.mp4 31.1 MB
3.Understanding RNN and LSTM/12.Why LSTM.mp4 52.1 MB
3.Understanding RNN and LSTM/13.Moving to LSTM.mp4 10.6 MB
4.Using Autoencoders for Fraud Detection/14.Getting Ready with Data.mp4 27.2 MB
4.Using Autoencoders for Fraud Detection/15.Developing a Model.mp4 27.8 MB
4.Using Autoencoders for Fraud Detection/16.Getting Output.mp4 8.6 MB
5.Recommending a Movie with Boltzmann Machines/17.Introduction to Boltzmann Machines.mp4 41.6 MB
5.Recommending a Movie with Boltzmann Machines/18.Getting Ready for Recommender System.mp4 18.5 MB
5.Recommending a Movie with Boltzmann Machines/19.Making Boltzmann Machines.mp4 60.9 MB
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Art/c-03.png 20.6 MB
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