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fileUdemy-Applied-Deep-Learn-1vXND

Udemy Applied Deep Learning Build Chatbot Theory Application
  • MP41. Theory Part 1 - RNNs and LSTMs/8. LSTM Step-by-Step Example Walktrough.mp422.76MB
  • MP41. Theory Part 1 - RNNs and LSTMs/7. LSTM Variants.mp423.50MB
  • MP42. Theory Part 2 - Sequence Modeling/3. How Attention Mechanis Work.mp436.78MB
  • MP42. Theory Part 2 - Sequence Modeling/2. Attention Mechanis.mp440.13MB
  • MP42. Theory Part 2 - Sequence Modeling/1. Sequence-to-Sequence Models.mp443.57MB
  • MP45. Practical Part 3 - Data Preperation/2. Understanding the zip function.mp445.38MB
  • MP47. Practical Part 5 - Training the Model/2. Teacher Forcing.mp448.89MB
  • MP46. Practical Part 4 - Building the Model/1. Understanding the Encoder.mp453.23MB
  • MP45. Practical Part 3 - Data Preperation/3. Preparing the Data for Model Part 2.mp454.96MB
  • MP44. Practical Part 2 - Processing the Dataset/5. Processing the Dataset Part 4.mp456.17MB
  • MP46. Practical Part 4 - Building the Model/3. Understanding Pack Padded Sequence.mp459.14MB
  • MP44. Practical Part 2 - Processing the Dataset/9. Filtering the Text.mp463.23MB
  • MP41. Theory Part 1 - RNNs and LSTMs/6. LSTMs.mp466.69MB
  • MP47. Practical Part 5 - Training the Model/1. Creating the Loss Function.mp467.47MB
  • MP41. Theory Part 1 - RNNs and LSTMs/3. Introduction to RNNs Part 2.mp467.84MB
  • MP43. Practical Part 1 - Introduction to PyTorch/3. Torch Tensors Part 2.mp467.95MB
  • VTT3. Practical Part 1 - Introduction to PyTorch/3. Torch Tensors Part 2.vtt67.96MB
  • MP44. Practical Part 2 - Processing the Dataset/2. Processing the Dataset Part 1.mp468.07MB
  • MP41. Theory Part 1 - RNNs and LSTMs/5. Playing with the Activations.mp471.58MB
  • MP43. Practical Part 1 - Introduction to PyTorch/1. Installing PyTorch and an Introduction.mp472.93MB
  • MP44. Practical Part 2 - Processing the Dataset/3. Processing the Data Part 2.mp473.84MB
  • MP44. Practical Part 2 - Processing the Dataset/4. Processing the Dataset Part 3.mp475.69MB
  • MP43. Practical Part 1 - Introduction to PyTorch/2. Torch Tensors Part 1.mp477.70MB
  • MP41. Theory Part 1 - RNNs and LSTMs/2. Introduction to RNNs Part 1.mp479.41MB
  • MP44. Practical Part 2 - Processing the Dataset/1. The Dataset.mp481.85MB
  • MP44. Practical Part 2 - Processing the Dataset/10. Getting Rid of Rare Words.mp482.54MB
  • MP45. Practical Part 3 - Data Preperation/1. Preparing the Data for Model Part 1.mp487.10MB
  • MP45. Practical Part 3 - Data Preperation/4. Preparing the Data for Model Part 3.mp488.57MB
  • MP44. Practical Part 2 - Processing the Dataset/6. Processing the Words.mp489.21MB
  • MP44. Practical Part 2 - Processing the Dataset/7. Processing the Text.mp495.63MB
  • MP44. Practical Part 2 - Processing the Dataset/8. Processing the Text Part 2.mp495.63MB
  • MP45. Practical Part 3 - Data Preperation/5. Preparing the Data for Model Part 4.mp4104.29MB
  • MP47. Practical Part 5 - Training the Model/4. Visualize Training Part 2.mp4113.13MB
  • MP47. Practical Part 5 - Training the Model/5. Training.mp4122.86MB
  • MP46. Practical Part 4 - Building the Model/5. Designing the Decoder Part 1.mp4127.27MB
  • MP47. Practical Part 5 - Training the Model/3. Visualize Training Part 1.mp4131.88MB
  • MP46. Practical Part 4 - Building the Model/4. Designing the Attention Model.mp4151.49MB
  • MP46. Practical Part 4 - Building the Model/6. Designing the Decoder Part 2.mp4160.16MB
  • MP46. Practical Part 4 - Building the Model/2. Defining the Encoder.mp4242.23MB
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