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

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Title: Udemy - Applied Deep Learning Build a ChatbotGroup: NOGRPSource: Udemy
Info Hash
3D7C30874D0B0BF65059DFC7AF6382ECA800DB44
Source
Unverified
Total Size
3.1 GB
Total Files
80
Seeders
1
Leechers
0
Health
1.00
Score
2
Type
Bookware

File List

FileSize
PaidCoursesForFree.com.url121 B
1. Theory Part 1 - RNNs and LSTMs/4. Test Your Understanding.html160 B
7. Practical Part 5 - Training the Model/6. Proceeding.html384 B
1. Theory Part 1 - RNNs and LSTMs/1. Before we Start.html1.04 KB
1. Theory Part 1 - RNNs and LSTMs/7. LSTM Variants.vtt3.91 KB
1. Theory Part 1 - RNNs and LSTMs/8. LSTM Step-by-Step Example Walktrough.vtt4.57 KB
5. Practical Part 3 - Data Preperation/2. Understanding the zip function.vtt6.37 KB
6. Practical Part 4 - Building the Model/1. Understanding the Encoder.vtt6.82 KB
2. Theory Part 2 - Sequence Modeling/2. Attention Mechanisms.vtt7.12 KB
7. Practical Part 5 - Training the Model/2. Teacher Forcing.vtt7.28 KB
7. Practical Part 5 - Training the Model/1. Creating the Loss Function.vtt7.44 KB
4. Practical Part 2 - Processing the Dataset/5. Processing the Dataset Part 4.vtt7.53 KB
4. Practical Part 2 - Processing the Dataset/2. Processing the Dataset Part 1.vtt7.59 KB
4. Practical Part 2 - Processing the Dataset/9. Filtering the Text.vtt7.81 KB
2. Theory Part 2 - Sequence Modeling/3. How Attention Mechanisms Work.vtt8.14 KB
6. Practical Part 4 - Building the Model/3. Understanding Pack Padded Sequence.vtt8.36 KB
4. Practical Part 2 - Processing the Dataset/3. Processing the Data Part 2.vtt9.27 KB
5. Practical Part 3 - Data Preperation/3. Preparing the Data for Model Part 2.vtt9.28 KB
1. Theory Part 1 - RNNs and LSTMs/3. Introduction to RNNs Part 2.vtt9.86 KB
4. Practical Part 2 - Processing the Dataset/8. Processing the Text Part 2.vtt10.08 KB
4. Practical Part 2 - Processing the Dataset/4. Processing the Dataset Part 3.vtt10.22 KB
2. Theory Part 2 - Sequence Modeling/1. Sequence-to-Sequence Models.vtt10.3 KB
4. Practical Part 2 - Processing the Dataset/10. Getting Rid of Rare Words.vtt10.3 KB
1. Theory Part 1 - RNNs and LSTMs/6. LSTMs.vtt10.69 KB
1. Theory Part 1 - RNNs and LSTMs/5. Playing with the Activations.vtt10.85 KB
4. Practical Part 2 - Processing the Dataset/7. Processing the Text.vtt10.87 KB
4. Practical Part 2 - Processing the Dataset/1. The Dataset.vtt11.17 KB
5. Practical Part 3 - Data Preperation/4. Preparing the Data for Model Part 3.vtt12.2 KB
3. Practical Part 1 - Introduction to PyTorch/1. Installing PyTorch and an Introduction.vtt12.64 KB
1. Theory Part 1 - RNNs and LSTMs/2. Introduction to RNNs Part 1.vtt12.66 KB
7. Practical Part 5 - Training the Model/4. Visualize Training Part 2.vtt12.66 KB
3. Practical Part 1 - Introduction to PyTorch/2. Torch Tensors Part 1.vtt13.21 KB
4. Practical Part 2 - Processing the Dataset/6. Processing the Words.vtt13.56 KB
5. Practical Part 3 - Data Preperation/1. Preparing the Data for Model Part 1.vtt14.21 KB
7. Practical Part 5 - Training the Model/5. Training.vtt14.36 KB
5. Practical Part 3 - Data Preperation/5. Preparing the Data for Model Part 4.vtt15.18 KB
7. Practical Part 5 - Training the Model/3. Visualize Training Part 1.vtt16.41 KB
6. Practical Part 4 - Building the Model/5. Designing the Decoder Part 1.vtt16.67 KB
6. Practical Part 4 - Building the Model/4. Designing the Attention Model.vtt18.3 KB
6. Practical Part 4 - Building the Model/6. Designing the Decoder Part 2.vtt20.38 KB
6. Practical Part 4 - Building the Model/2. Defining the Encoder.vtt28.05 KB
1. Theory Part 1 - RNNs and LSTMs/8. LSTM Step-by-Step Example Walktrough.mp422.76 MB
1. Theory Part 1 - RNNs and LSTMs/7. LSTM Variants.mp423.5 MB
2. Theory Part 2 - Sequence Modeling/3. How Attention Mechanisms Work.mp436.78 MB
2. Theory Part 2 - Sequence Modeling/2. Attention Mechanisms.mp440.13 MB
2. Theory Part 2 - Sequence Modeling/1. Sequence-to-Sequence Models.mp443.57 MB
5. Practical Part 3 - Data Preperation/2. Understanding the zip function.mp445.38 MB
7. Practical Part 5 - Training the Model/2. Teacher Forcing.mp448.89 MB
6. Practical Part 4 - Building the Model/1. Understanding the Encoder.mp453.23 MB
5. Practical Part 3 - Data Preperation/3. Preparing the Data for Model Part 2.mp454.96 MB
4. Practical Part 2 - Processing the Dataset/5. Processing the Dataset Part 4.mp456.17 MB
6. Practical Part 4 - Building the Model/3. Understanding Pack Padded Sequence.mp459.14 MB
4. Practical Part 2 - Processing the Dataset/9. Filtering the Text.mp463.23 MB
1. Theory Part 1 - RNNs and LSTMs/6. LSTMs.mp466.69 MB
7. Practical Part 5 - Training the Model/1. Creating the Loss Function.mp467.47 MB
1. Theory Part 1 - RNNs and LSTMs/3. Introduction to RNNs Part 2.mp467.84 MB
3. Practical Part 1 - Introduction to PyTorch/3. Torch Tensors Part 2.mp467.95 MB
3. Practical Part 1 - Introduction to PyTorch/3. Torch Tensors Part 2.vtt67.96 MB
4. Practical Part 2 - Processing the Dataset/2. Processing the Dataset Part 1.mp468.07 MB
1. Theory Part 1 - RNNs and LSTMs/5. Playing with the Activations.mp471.58 MB
3. Practical Part 1 - Introduction to PyTorch/1. Installing PyTorch and an Introduction.mp472.93 MB
4. Practical Part 2 - Processing the Dataset/3. Processing the Data Part 2.mp473.84 MB
4. Practical Part 2 - Processing the Dataset/4. Processing the Dataset Part 3.mp475.69 MB
3. Practical Part 1 - Introduction to PyTorch/2. Torch Tensors Part 1.mp477.7 MB
1. Theory Part 1 - RNNs and LSTMs/2. Introduction to RNNs Part 1.mp479.41 MB
4. Practical Part 2 - Processing the Dataset/1. The Dataset.mp481.85 MB
4. Practical Part 2 - Processing the Dataset/10. Getting Rid of Rare Words.mp482.54 MB
5. Practical Part 3 - Data Preperation/1. Preparing the Data for Model Part 1.mp487.1 MB
5. Practical Part 3 - Data Preperation/4. Preparing the Data for Model Part 3.mp488.57 MB
4. Practical Part 2 - Processing the Dataset/6. Processing the Words.mp489.21 MB
4. Practical Part 2 - Processing the Dataset/7. Processing the Text.mp495.63 MB
4. Practical Part 2 - Processing the Dataset/8. Processing the Text Part 2.mp495.63 MB
5. Practical Part 3 - Data Preperation/5. Preparing the Data for Model Part 4.mp4104.29 MB
7. Practical Part 5 - Training the Model/4. Visualize Training Part 2.mp4113.13 MB
7. Practical Part 5 - Training the Model/5. Training.mp4122.86 MB
6. Practical Part 4 - Building the Model/5. Designing the Decoder Part 1.mp4127.27 MB
7. Practical Part 5 - Training the Model/3. Visualize Training Part 1.mp4131.88 MB
6. Practical Part 4 - Building the Model/4. Designing the Attention Model.mp4151.49 MB
6. Practical Part 4 - Building the Model/6. Designing the Decoder Part 2.mp4160.16 MB
6. Practical Part 4 - Building the Model/2. Defining the Encoder.mp4242.23 MB

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