[PaidCoursesForFree.com] - Udemy - Applied Deep Learning Build a Chatbot - Theory, Application

磁力链接/BT种子名称

[PaidCoursesForFree.com] - Udemy - Applied Deep Learning Build a Chatbot - Theory, Application

磁力链接/BT种子简介

种子哈希：3d7c30874d0b0bf65059dfc7af6382eca800db44
文件大小： 3.1G
已经下载：1323次
下载速度：极快
收录时间：2021-05-08
最近下载：2025-10-20

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文件列表

  • 6. Practical Part 4 - Building the Model/2. Defining the Encoder.mp4 254.0 MB
  • 6. Practical Part 4 - Building the Model/6. Designing the Decoder Part 2.mp4 167.9 MB
  • 6. Practical Part 4 - Building the Model/4. Designing the Attention Model.mp4 158.8 MB
  • 7. Practical Part 5 - Training the Model/3. Visualize Training Part 1.mp4 138.3 MB
  • 6. Practical Part 4 - Building the Model/5. Designing the Decoder Part 1.mp4 133.5 MB
  • 7. Practical Part 5 - Training the Model/5. Training.mp4 128.8 MB
  • 7. Practical Part 5 - Training the Model/4. Visualize Training Part 2.mp4 118.6 MB
  • 5. Practical Part 3 - Data Preperation/5. Preparing the Data for Model Part 4.mp4 109.4 MB
  • 4. Practical Part 2 - Processing the Dataset/8. Processing the Text Part 2.mp4 100.3 MB
  • 4. Practical Part 2 - Processing the Dataset/7. Processing the Text.mp4 100.3 MB
  • 4. Practical Part 2 - Processing the Dataset/6. Processing the Words.mp4 93.5 MB
  • 5. Practical Part 3 - Data Preperation/4. Preparing the Data for Model Part 3.mp4 92.9 MB
  • 5. Practical Part 3 - Data Preperation/1. Preparing the Data for Model Part 1.mp4 91.3 MB
  • 4. Practical Part 2 - Processing the Dataset/10. Getting Rid of Rare Words.mp4 86.5 MB
  • 4. Practical Part 2 - Processing the Dataset/1. The Dataset.mp4 85.8 MB
  • 1. Theory Part 1 - RNNs and LSTMs/2. Introduction to RNNs Part 1.mp4 83.3 MB
  • 3. Practical Part 1 - Introduction to PyTorch/2. Torch Tensors Part 1.mp4 81.5 MB
  • 4. Practical Part 2 - Processing the Dataset/4. Processing the Dataset Part 3.mp4 79.4 MB
  • 4. Practical Part 2 - Processing the Dataset/3. Processing the Data Part 2.mp4 77.4 MB
  • 3. Practical Part 1 - Introduction to PyTorch/1. Installing PyTorch and an Introduction.mp4 76.5 MB
  • 1. Theory Part 1 - RNNs and LSTMs/5. Playing with the Activations.mp4 75.1 MB
  • 4. Practical Part 2 - Processing the Dataset/2. Processing the Dataset Part 1.mp4 71.4 MB
  • 3. Practical Part 1 - Introduction to PyTorch/3. Torch Tensors Part 2.vtt 71.3 MB
  • 3. Practical Part 1 - Introduction to PyTorch/3. Torch Tensors Part 2.mp4 71.3 MB
  • 1. Theory Part 1 - RNNs and LSTMs/3. Introduction to RNNs Part 2.mp4 71.1 MB
  • 7. Practical Part 5 - Training the Model/1. Creating the Loss Function.mp4 70.7 MB
  • 1. Theory Part 1 - RNNs and LSTMs/6. LSTMs.mp4 69.9 MB
  • 4. Practical Part 2 - Processing the Dataset/9. Filtering the Text.mp4 66.3 MB
  • 6. Practical Part 4 - Building the Model/3. Understanding Pack Padded Sequence.mp4 62.0 MB
  • 4. Practical Part 2 - Processing the Dataset/5. Processing the Dataset Part 4.mp4 58.9 MB
  • 5. Practical Part 3 - Data Preperation/3. Preparing the Data for Model Part 2.mp4 57.6 MB
  • 6. Practical Part 4 - Building the Model/1. Understanding the Encoder.mp4 55.8 MB
  • 7. Practical Part 5 - Training the Model/2. Teacher Forcing.mp4 51.3 MB
  • 5. Practical Part 3 - Data Preperation/2. Understanding the zip function.mp4 47.6 MB
  • 2. Theory Part 2 - Sequence Modeling/1. Sequence-to-Sequence Models.mp4 45.7 MB
  • 2. Theory Part 2 - Sequence Modeling/2. Attention Mechanisms.mp4 42.1 MB
  • 2. Theory Part 2 - Sequence Modeling/3. How Attention Mechanisms Work.mp4 38.6 MB
  • 1. Theory Part 1 - RNNs and LSTMs/7. LSTM Variants.mp4 24.6 MB
  • 1. Theory Part 1 - RNNs and LSTMs/8. LSTM Step-by-Step Example Walktrough.mp4 23.9 MB
  • 6. Practical Part 4 - Building the Model/2. Defining the Encoder.vtt 28.7 kB
  • 6. Practical Part 4 - Building the Model/6. Designing the Decoder Part 2.vtt 20.9 kB
  • 6. Practical Part 4 - Building the Model/4. Designing the Attention Model.vtt 18.7 kB
  • 6. Practical Part 4 - Building the Model/5. Designing the Decoder Part 1.vtt 17.1 kB
  • 7. Practical Part 5 - Training the Model/3. Visualize Training Part 1.vtt 16.8 kB
  • 5. Practical Part 3 - Data Preperation/5. Preparing the Data for Model Part 4.vtt 15.5 kB
  • 7. Practical Part 5 - Training the Model/5. Training.vtt 14.7 kB
  • 5. Practical Part 3 - Data Preperation/1. Preparing the Data for Model Part 1.vtt 14.6 kB
  • 4. Practical Part 2 - Processing the Dataset/6. Processing the Words.vtt 13.9 kB
  • 3. Practical Part 1 - Introduction to PyTorch/2. Torch Tensors Part 1.vtt 13.5 kB
  • 7. Practical Part 5 - Training the Model/4. Visualize Training Part 2.vtt 13.0 kB
  • 1. Theory Part 1 - RNNs and LSTMs/2. Introduction to RNNs Part 1.vtt 13.0 kB
  • 3. Practical Part 1 - Introduction to PyTorch/1. Installing PyTorch and an Introduction.vtt 12.9 kB
  • 5. Practical Part 3 - Data Preperation/4. Preparing the Data for Model Part 3.vtt 12.5 kB
  • 4. Practical Part 2 - Processing the Dataset/1. The Dataset.vtt 11.4 kB
  • 4. Practical Part 2 - Processing the Dataset/7. Processing the Text.vtt 11.1 kB
  • 1. Theory Part 1 - RNNs and LSTMs/5. Playing with the Activations.vtt 11.1 kB
  • 1. Theory Part 1 - RNNs and LSTMs/6. LSTMs.vtt 10.9 kB
  • 4. Practical Part 2 - Processing the Dataset/10. Getting Rid of Rare Words.vtt 10.5 kB
  • 2. Theory Part 2 - Sequence Modeling/1. Sequence-to-Sequence Models.vtt 10.5 kB
  • 4. Practical Part 2 - Processing the Dataset/4. Processing the Dataset Part 3.vtt 10.5 kB
  • 4. Practical Part 2 - Processing the Dataset/8. Processing the Text Part 2.vtt 10.3 kB
  • 1. Theory Part 1 - RNNs and LSTMs/3. Introduction to RNNs Part 2.vtt 10.1 kB
  • 5. Practical Part 3 - Data Preperation/3. Preparing the Data for Model Part 2.vtt 9.5 kB
  • 4. Practical Part 2 - Processing the Dataset/3. Processing the Data Part 2.vtt 9.5 kB
  • 6. Practical Part 4 - Building the Model/3. Understanding Pack Padded Sequence.vtt 8.6 kB
  • 2. Theory Part 2 - Sequence Modeling/3. How Attention Mechanisms Work.vtt 8.3 kB
  • 4. Practical Part 2 - Processing the Dataset/9. Filtering the Text.vtt 8.0 kB
  • 4. Practical Part 2 - Processing the Dataset/2. Processing the Dataset Part 1.vtt 7.8 kB
  • 4. Practical Part 2 - Processing the Dataset/5. Processing the Dataset Part 4.vtt 7.7 kB
  • 7. Practical Part 5 - Training the Model/1. Creating the Loss Function.vtt 7.6 kB
  • 7. Practical Part 5 - Training the Model/2. Teacher Forcing.vtt 7.5 kB
  • 2. Theory Part 2 - Sequence Modeling/2. Attention Mechanisms.vtt 7.3 kB
  • 6. Practical Part 4 - Building the Model/1. Understanding the Encoder.vtt 7.0 kB
  • 5. Practical Part 3 - Data Preperation/2. Understanding the zip function.vtt 6.5 kB
  • 1. Theory Part 1 - RNNs and LSTMs/8. LSTM Step-by-Step Example Walktrough.vtt 4.7 kB
  • 1. Theory Part 1 - RNNs and LSTMs/7. LSTM Variants.vtt 4.0 kB
  • 1. Theory Part 1 - RNNs and LSTMs/1. Before we Start.html 1.1 kB
  • 7. Practical Part 5 - Training the Model/6. Proceeding.html 384 Bytes
  • 1. Theory Part 1 - RNNs and LSTMs/4. Test Your Understanding.html 160 Bytes
  • PaidCoursesForFree.com.url 121 Bytes

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