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种子名称：
[PaidCoursesForFree.com] - Udemy - Applied Deep Learning Build a Chatbot - Theory, Application
文件类型：
视频
文件数目：
38个文件
文件大小：
3.04 GB
收录时间：
2020-8-4 01:54
已经下载：
3次
资源热度：
343
最近下载：
2024-12-31 16:10
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种子包含的文件
[PaidCoursesForFree.com] - Udemy - Applied Deep Learning Build a Chatbot - Theory, Application.torrent
1. Theory Part 1 - RNNs and LSTMs/8. LSTM Step-by-Step Example Walktrough.mp422.76MB
1. Theory Part 1 - RNNs and LSTMs/7. LSTM Variants.mp423.5MB
2. Theory Part 2 - Sequence Modeling/3. How Attention Mechanisms Work.mp436.78MB
2. Theory Part 2 - Sequence Modeling/2. Attention Mechanisms.mp440.13MB
2. Theory Part 2 - Sequence Modeling/1. Sequence-to-Sequence Models.mp443.57MB
5. Practical Part 3 - Data Preperation/2. Understanding the zip function.mp445.38MB
7. Practical Part 5 - Training the Model/2. Teacher Forcing.mp448.89MB
6. Practical Part 4 - Building the Model/1. Understanding the Encoder.mp453.23MB
5. Practical Part 3 - Data Preperation/3. Preparing the Data for Model Part 2.mp454.96MB
4. Practical Part 2 - Processing the Dataset/5. Processing the Dataset Part 4.mp456.17MB
6. Practical Part 4 - Building the Model/3. Understanding Pack Padded Sequence.mp459.14MB
4. Practical Part 2 - Processing the Dataset/9. Filtering the Text.mp463.23MB
1. Theory Part 1 - RNNs and LSTMs/6. LSTMs.mp466.69MB
7. Practical Part 5 - Training the Model/1. Creating the Loss Function.mp467.47MB
1. Theory Part 1 - RNNs and LSTMs/3. Introduction to RNNs Part 2.mp467.84MB
3. Practical Part 1 - Introduction to PyTorch/3. Torch Tensors Part 2.mp467.95MB
4. Practical Part 2 - Processing the Dataset/2. Processing the Dataset Part 1.mp468.07MB
1. Theory Part 1 - RNNs and LSTMs/5. Playing with the Activations.mp471.58MB
3. Practical Part 1 - Introduction to PyTorch/1. Installing PyTorch and an Introduction.mp472.93MB
4. Practical Part 2 - Processing the Dataset/3. Processing the Data Part 2.mp473.84MB
4. Practical Part 2 - Processing the Dataset/4. Processing the Dataset Part 3.mp475.69MB
3. Practical Part 1 - Introduction to PyTorch/2. Torch Tensors Part 1.mp477.7MB
1. Theory Part 1 - RNNs and LSTMs/2. Introduction to RNNs Part 1.mp479.41MB
4. Practical Part 2 - Processing the Dataset/1. The Dataset.mp481.85MB
4. Practical Part 2 - Processing the Dataset/10. Getting Rid of Rare Words.mp482.54MB
5. Practical Part 3 - Data Preperation/1. Preparing the Data for Model Part 1.mp487.1MB
5. Practical Part 3 - Data Preperation/4. Preparing the Data for Model Part 3.mp488.57MB
4. Practical Part 2 - Processing the Dataset/6. Processing the Words.mp489.21MB
4. Practical Part 2 - Processing the Dataset/7. Processing the Text.mp495.63MB
4. Practical Part 2 - Processing the Dataset/8. Processing the Text Part 2.mp495.63MB
5. Practical Part 3 - Data Preperation/5. Preparing the Data for Model Part 4.mp4104.29MB
7. Practical Part 5 - Training the Model/4. Visualize Training Part 2.mp4113.13MB
7. Practical Part 5 - Training the Model/5. Training.mp4122.86MB
6. Practical Part 4 - Building the Model/5. Designing the Decoder Part 1.mp4127.27MB
7. Practical Part 5 - Training the Model/3. Visualize Training Part 1.mp4131.88MB
6. Practical Part 4 - Building the Model/4. Designing the Attention Model.mp4151.49MB
6. Practical Part 4 - Building the Model/6. Designing the Decoder Part 2.mp4160.16MB
6. Practical Part 4 - Building the Model/2. Defining the Encoder.mp4242.23MB
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