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[ DevCourseWeb com ] Udemy - Deep Learning with Google Colab
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1. Getting started in Google Colab
1. Introduction.mp4
1. Introduction.srt
10. Section conclusion.mp4
10. Section conclusion.srt
2. Registering for a Google account.mp4
2. Registering for a Google account.srt
3. Navigating to Google Colab.mp4
3. Navigating to Google Colab.srt
4. Exploring your Google Colab Notebook.mp4
4. Exploring your Google Colab Notebook.srt
5. The definition of notebooks.mp4
5. The definition of notebooks.srt
6. Running your first Google Colab code cell.mp4
6. Running your first Google Colab code cell.srt
7. The markup language Markdown.mp4
7. The markup language Markdown.srt
8. Writing Markdown in Google Colab.mp4
8. Writing Markdown in Google Colab.srt
9. Writing LaTeX in Google Colab.mp4
9. Writing LaTeX in Google Colab.srt
2. The ecosystem of Google Colab
1. Installing packages in Google Colab.mp4
1. Installing packages in Google Colab.srt
2. Working with files using Google Drive.mp4
2. Working with files using Google Drive.srt
3. Working with files directly in Google Colab.mp4
3. Working with files directly in Google Colab.srt
4. Sharing files via Google Drive.mp4
4. Sharing files via Google Drive.srt
5. Introduction to version control with Git and GitHub.mp4
5. Introduction to version control with Git and GitHub.srt
6. Sending Google Colab notebooks to GitHub.mp4
6. Sending Google Colab notebooks to GitHub.srt
3. Introduction to PyTorch
1. Creating a tensor.mp4
1. Creating a tensor.srt
10. Saving and loading models.mp4
10. Saving and loading models.srt
11. Problem statement and setup.mp4
11. Problem statement and setup.srt
12. Approaches and solutions.mp4
12. Approaches and solutions.srt
2. Tensor operations.mp4
2. Tensor operations.srt
3. GPUs in the context of deep learning.mp4
3. GPUs in the context of deep learning.srt
4. Turning on your Colab GPU.mp4
4. Turning on your Colab GPU.srt
5. Limits of the Colab GPU.mp4
5. Limits of the Colab GPU.srt
6. Neural network basics.mp4
6. Neural network basics.srt
7. Gradients and backpropagation.mp4
7. Gradients and backpropagation.srt
8. Automatic differentiation in PyTorch.mp4
8. Automatic differentiation in PyTorch.srt
9. Training a model.mp4
9. Training a model.srt
4. Working with datasets
1. Downloading a built-in dataset.mp4
1. Downloading a built-in dataset.srt
2. Working with PyTorch datasets.mp4
2. Working with PyTorch datasets.srt
3. Loading a dataset into Colab.mp4
3. Loading a dataset into Colab.srt
4. Building a PyTorch dataset.mp4
4. Building a PyTorch dataset.srt
5. Image augmentation fundamentals.mp4
5. Image augmentation fundamentals.srt
6. Image augmentation in PyTorch.mp4
6. Image augmentation in PyTorch.srt
5. Recognizing handwritten digits
1. Downloading the dataset.mp4
1. Downloading the dataset.srt
2. Understanding the dataset.mp4
2. Understanding the dataset.srt
3. Implementing a starting solution.mp4
3. Implementing a starting solution.srt
4. Training and evaluating.mp4
4. Training and evaluating.srt
5. Choosing the size of input and output layers.mp4
5. Choosing the size of input and output layers.srt
6. Choosing the size of hidden layers.mp4
6. Choosing the size of hidden layers.srt
7. Loss functions.mp4
7. Loss functions.srt
8. Activation functions and weight initialization.mp4
8. Activation functions and weight initialization.srt
9. Optimizers.mp4
9. Optimizers.srt
6. Transfer learning for object recognition
1. Downloading the dataset.mp4
1. Downloading the dataset.srt
2. Understanding the dataset.mp4
2. Understanding the dataset.srt
3. What is transfer learning.mp4
3. What is transfer learning.srt
4. The transfer learning workflow.mp4
4. The transfer learning workflow.srt
5. Training and evaluating.mp4
5. Training and evaluating.srt
6. Pretrained models for transfer learning.mp4
6. Pretrained models for transfer learning.srt
7. Recognizing fashion items
1. Downloading the dataset.mp4
1. Downloading the dataset.srt
2. Understanding the dataset.mp4
2. Understanding the dataset.srt
3. Convolutional network fundamentals.mp4
3. Convolutional network fundamentals.srt
4. Implementation in PyTorch.mp4
4. Implementation in PyTorch.srt
5. Residual network fundamentals.mp4
5. Residual network fundamentals.srt
6. Residual blocks in convolutional networks.mp4
6. Residual blocks in convolutional networks.srt
7. Implementation in PyTorch.mp4
7. Implementation in PyTorch.srt
8. Deep learning best practices
1. General ensembling in machine learning.mp4
1. General ensembling in machine learning.srt
2. Ensembling in deep learning.mp4
2. Ensembling in deep learning.srt
3. Data versioning.mp4
3. Data versioning.srt
4. Reproducibility.mp4
4. Reproducibility.srt
5. When not to use deep learning.mp4
5. When not to use deep learning.srt
Bonus Resources.txt
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