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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
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3. Practical Part 1 - Introduction to PyTorch/2. Torch Tensors Part 1.mp4 81.5 MB
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27. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4 226.5 MB
37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.mp4 173.2 MB
18. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4 169.1 MB
26. ANN in Python/9. Building Neural Network for Regression Problem.mp4 163.5 MB
26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4 158.9 MB
23. Creating Support Vector Machine Model in R/3. Classification SVM model using Linear Kernel.mp4 145.9 MB
27. ANN in R/6. Building Regression Model with Functional API.mp4 137.5 MB
27. ANN in R/3. Building,Compiling and Training.mp4 137.1 MB
34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4 135.4 MB
7. Linear Regression/20. Ridge regression and Lasso in Python.mp4 135.1 MB
25. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 128.1 MB
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27. ANN in R/2. Data Normalization and Test-Train Split.mp4 117.2 MB
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37. Time Series - Preprocessing in Python/1. Data Loading in Python.mp4 114.1 MB
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