| 1. Creating a tensor.mp4 | 61.1 MB | ||
| 1. Creating a tensor.srt | 9.6 KB | ||
| 1. Downloading a built-in dataset.mp4 | 39.5 MB | ||
| 1. Downloading a built-in dataset.srt | 7.7 KB | ||
| 1. Downloading the dataset.mp4 | 49.8 MB | ||
| 1. Downloading the dataset.srt | 7.2 KB | ||
| 1. General ensembling in machine learning.mp4 | 69.6 MB | ||
| 1. General ensembling in machine learning.srt | 8.7 KB | ||
| 1. Installing packages in Google Colab.mp4 | 37.1 MB | ||
| 1. Installing packages in Google Colab.srt | 5.2 KB | ||
| 1. Introduction.mp4 | 42 MB | ||
| 1. Introduction.srt | 3.7 KB | ||
| 10. Saving and loading models.mp4 | 58.4 MB | ||
| 10. Saving and loading models.srt | 10.6 KB | ||
| 10. Section conclusion.mp4 | 14.2 MB | ||
| 10. Section conclusion.srt | 1.3 KB | ||
| 11. Problem statement and setup.mp4 | 20.1 MB | ||
| 11. Problem statement and setup.srt | 4.9 KB | ||
| 12. Approaches and solutions.mp4 | 52.8 MB | ||
| 12. Approaches and solutions.srt | 9.7 KB | ||
| 2. Ensembling in deep learning.mp4 | 65.9 MB | ||
| 2. Ensembling in deep learning.srt | 10.3 KB | ||
| 2. Registering for a Google account.mp4 | 7.7 MB | ||
| 2. Registering for a Google account.srt | 1.7 KB | ||
| 2. Tensor operations.mp4 | 40.5 MB | ||
| 2. Tensor operations.srt | 8.2 KB | ||
| 2. Understanding the dataset.mp4 | 33 MB | ||
| 2. Understanding the dataset.srt | 7.2 KB | ||
| 2. Working with PyTorch datasets.mp4 | 69.7 MB | ||
| 2. Working with PyTorch datasets.srt | 9.2 KB | ||
| 2. Working with files using Google Drive.mp4 | 35.1 MB | ||
| 2. Working with files using Google Drive.srt | 5.6 KB | ||
| 3. Convolutional network fundamentals.mp4 | 87.7 MB | ||
| 3. Convolutional network fundamentals.srt | 11 KB | ||
| 3. Data versioning.mp4 | 45.4 MB | ||
| 3. Data versioning.srt | 6.8 KB | ||
| 3. GPUs in the context of deep learning.mp4 | 70.6 MB | ||
| 3. GPUs in the context of deep learning.srt | 6.8 KB | ||
| 3. Implementing a starting solution.mp4 | 60.4 MB | ||
| 3. Implementing a starting solution.srt | 10.7 KB | ||
| 3. Loading a dataset into Colab.mp4 | 32.9 MB | ||
| 3. Loading a dataset into Colab.srt | 6.2 KB | ||
| 3. Navigating to Google Colab.mp4 | 16.3 MB | ||
| 3. Navigating to Google Colab.srt | 2.3 KB | ||
| 3. What is transfer learning.mp4 | 92.2 MB | ||
| 3. What is transfer learning.srt | 11.2 KB | ||
| 3. Working with files directly in Google Colab.mp4 | 40.8 MB | ||
| 3. Working with files directly in Google Colab.srt | 6.5 KB | ||
| 4. Building a PyTorch dataset.mp4 | 65.9 MB | ||
| 4. Building a PyTorch dataset.srt | 11.6 KB | ||
| 4. Exploring your Google Colab Notebook.mp4 | 15.4 MB | ||
| 4. Exploring your Google Colab Notebook.srt | 2.6 KB | ||
| 4. Implementation in PyTorch.mp4 | 48.8 MB | ||
| 4. Implementation in PyTorch.srt | 9.6 KB | ||
| 4. Reproducibility.mp4 | 28.3 MB | ||
| 4. Reproducibility.srt | 4.4 KB | ||
| 4. Sharing files via Google Drive.mp4 | 32.5 MB | ||
| 4. Sharing files via Google Drive.srt | 5.7 KB | ||
| 4. The transfer learning workflow.mp4 | 68.2 MB | ||
| 4. The transfer learning workflow.srt | 9.3 KB | ||
| 4. Training and evaluating.mp4 | 29.6 MB | ||
| 4. Training and evaluating.srt | 6.6 KB | ||
| 4. Turning on your Colab GPU.mp4 | 19 MB | ||
| 4. Turning on your Colab GPU.srt | 6 KB | ||
| 5. Choosing the size of input and output layers.mp4 | 53.8 MB | ||
| 5. Choosing the size of input and output layers.srt | 6.5 KB | ||
| 5. Image augmentation fundamentals.mp4 | 69 MB | ||
| 5. Image augmentation fundamentals.srt | 9.6 KB | ||
| 5. Introduction to version control with Git and GitHub.mp4 | 58.2 MB | ||
| 5. Introduction to version control with Git and GitHub.srt | 5.9 KB | ||
| 5. Limits of the Colab GPU.mp4 | 29.9 MB | ||
| 5. Limits of the Colab GPU.srt | 4.6 KB | ||
| 5. Residual network fundamentals.mp4 | 57.1 MB | ||
| 5. Residual network fundamentals.srt | 7.6 KB | ||
| 5. The definition of notebooks.mp4 | 11.9 MB | ||
| 5. The definition of notebooks.srt | 1.5 KB | ||
| 5. Training and evaluating.mp4 | 41 MB | ||
| 5. Training and evaluating.srt | 8.9 KB | ||
| 5. When not to use deep learning.mp4 | 66.3 MB | ||
| 5. When not to use deep learning.srt | 8.8 KB | ||
| 6. Choosing the size of hidden layers.mp4 | 87.9 MB | ||
| 6. Choosing the size of hidden layers.srt | 12.6 KB | ||
| 6. Image augmentation in PyTorch.mp4 | 81.9 MB | ||
| 6. Image augmentation in PyTorch.srt | 12.9 KB | ||
| 6. Neural network basics.mp4 | 37.4 MB | ||
| 6. Neural network basics.srt | 4.8 KB | ||
| 6. Pretrained models for transfer learning.mp4 | 62.8 MB | ||
| 6. Pretrained models for transfer learning.srt | 8.2 KB | ||
| 6. Residual blocks in convolutional networks.mp4 | 39.2 MB | ||
| 6. Residual blocks in convolutional networks.srt | 6.6 KB | ||
| 6. Running your first Google Colab code cell.mp4 | 19.8 MB | ||
| 6. Running your first Google Colab code cell.srt | 5.1 KB | ||
| 6. Sending Google Colab notebooks to GitHub.mp4 | 59.9 MB | ||
| 6. Sending Google Colab notebooks to GitHub.srt | 9.4 KB | ||
| 7. Gradients and backpropagation.mp4 | 76.6 MB | ||
| 7. Gradients and backpropagation.srt | 10.3 KB | ||
| 7. Implementation in PyTorch.mp4 | 42.8 MB | ||
| 7. Implementation in PyTorch.srt | 7 KB | ||
| 7. Loss functions.mp4 | 68.9 MB | ||
| 7. Loss functions.srt | 8.9 KB | ||
| 7. The markup language Markdown.mp4 | 15.8 MB | ||
| 7. The markup language Markdown.srt | 2.4 KB | ||
| 8. Activation functions and weight initialization.mp4 | 76.6 MB | ||
| 8. Activation functions and weight initialization.srt | 10.3 KB | ||
| 8. Automatic differentiation in PyTorch.mp4 | 43.1 MB | ||
| 8. Automatic differentiation in PyTorch.srt | 9.5 KB | ||
| 8. Writing Markdown in Google Colab.mp4 | 12.5 MB | ||
| 8. Writing Markdown in Google Colab.srt | 3.1 KB | ||
| 9. Optimizers.mp4 | 90.2 MB | ||
| 9. Optimizers.srt | 10.7 KB | ||
| 9. Training a model.mp4 | 45.1 MB | ||
| 9. Training a model.srt | 11.2 KB | ||
| 9. Writing LaTeX in Google Colab.mp4 | 17.6 MB | ||
| 9. Writing LaTeX in Google Colab.srt | 1.8 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 124 total files | |||
Deep Learning with Google Colab
https://DevCourseWeb.com
Last updated 2/2020
Duration: 5h 43m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 2.8 GB
Genre: eLearning | Language: English
Implementing and training deep learning models in a free, integrated environment
What you'll learn
This course covers the general workflow of a deep learning project, implemented using PyTorch in Google Colab. At the end of the course, students will be proficient at using Google Colab as well as PyTorch in their own projects. Students will also learn about the theoretical foundations for various deep learning models and techniques, as well as how to implement them using PyTorch. Finally, the course ends by offering an overview on general deep learning and how to think about problems in the field; students will gain a high-level understanding of the role deep learning plays in the field of AI.
Learn how to utilize Google Colab as an online computing platform in deep learning projects, including running Python code, using a free GPU, and working with external files and folders
Understand the general workflow of a deep learning project
Examine the various APIs (datasets, modeling, training) PyTorch offers to facilitate deep learning
Learn about the theoretical basis for various deep learning models such as convolutional networks or residual networks and what problems they address
Gain an overview understanding of deep learning in the context of the artificial intelligence field and its best practices
Requirements
Familiarity with Python programming (including classes, functions, context managers)
Basic linear algebra and calculus (briefly used during the discussions on various deep learning models and techniques)
Description
This course covers the general workflow of a deep learning project, implemented using PyTorch in Google Colab. At the end of the course, students will be proficient at using Google Colab as well as PyTorch in their own projects. Students will also learn about the theoretical foundations for various deep learning models and techniques, as well as how to implement them using PyTorch. Finally, the course ends by offering an overview on general deep learning and how to think about problems in the field; students will gain a high-level understanding of the role deep learning plays in the field of AI.
Learn how to utilize Google Colab as an online computing platform in deep learning projects, including running Python code, using a free GPU, and working with external files and folders
Understand the general workflow of a deep learning project
Examine the various APIs (datasets, modeling, training) PyTorch offers to facilitate deep learning
Learn about the theoretical basis for various deep learning models such as convolutional networks or residual networks and what problems they address
Gain an overview understanding of deep learning in the context of the artificial intelligence field and its best practices
Who this course is for
AI enthusiasts interested in getting started on deep learning
Programmers familiar with deep learning looking to gain a comprehensive understanding of various deep learning models and techniques
Homepage
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 2.5 GB | freecoursewb | 5 months | 0 | 0 | |
| 4 GB | freecoursewb | 5 months | 24 | 10 | |
| 582.4 MB | freecoursewb | 7 months | 0 | 0 | |
|
Udemy - AWS Networking Deep-Dive Crash Course - Master VPC Essentials Posted by
freecoursewb in Other
|
1.8 GB | freecoursewb | 7 months | 4 | 2 |
| 1.7 GB | freecoursewb | 7 months | 5 | 1 |
All Comments