4 out of 5
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40 reviews on Udemy

Deep Learning with Google Colab

Implementing and training deep learning models in a free, integrated environment
Instructor:
BPB Online + 100 Million Books Sold
7,272 students enrolled
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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

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

Getting started in Google Colab

1
Introduction

1. This lecture discusses the initial process of creating a Google account. As most Google applications are tied to this account, students will also have access to Google Drive, Gmail, etc.

- Know how to register for a Google account

- Know how to navigate to the Colab application

2
Registering for a Google account

A hands-on tutorial on how to register for a Google account.

3
Navigating to Google Colab

How to navigate to the Google Colab application within the Google workspace.

4
Exploring your Google Colab Notebook

Exploration of various features in a Google Colab notebook.

5
The definition of notebooks

Introduction to the concept of a computer notebook.

6
Running your first Google Colab code cell

Executing Python code in a Colab notebook.

7
The markup language Markdown

Introduction to the markup language Markdown.

8
Writing Markdown in Google Colab

How to write Markdown code in a Colab notebook.

9
Writing LaTeX in Google Colab

How to write LaTeX code in a Colab notebook.

10
Section conclusion

The ecosystem of Google Colab

1
Installing packages in Google Colab

How to install external packages in a Colab notebook.

2
Working with files using Google Drive

How to work with files via Google Drive in a Colab notebook.

3
Working with files directly in Google Colab

How to work with files via Python code in a Colab notebook.

4
Sharing files via Google Drive

How to share files with other users.

5
Introduction to version control with Git and GitHub

Introduction to the concept of version control with Git and GitHub.

6
Sending Google Colab notebooks to GitHub

How to facilitate version control with a Colab notebooks.

Introduction to PyTorch

1
Creating a tensor

How to create a tensor object in PyTorch.

2
Tensor operations

How to apply operations on tensor objects in PyTorch.

3
GPUs in the context of deep learning

Introduction to Graphical Processing Units and why they can be used in deep learning.

4
Turning on your Colab GPU

How to utilize the free GPU provided in each Colab notebook.

5
Limits of the Colab GPU

Various limitations that the free GPU has.

6
Neural network basics

Introduction to neural networks.

7
Gradients and backpropagation

Gradients and how neural networks learn.

8
Automatic differentiation in PyTorch

How to facilitate automatic differentiation in PyTorch.

9
Training a model

How to train a PyTorch model from scratch.

10
Saving and loading models

How to save a trained model and load it back in a Python program.

11
Problem statement and setup

Introduction to a sample curve-fitting problem.

12
Approaches and solutions

Discussion of potential solutions to the curve-fitting problem.

Working with datasets

1
Downloading a built-in dataset

How to download a built-in dataset with PyTorch.

2
Working with PyTorch datasets

Introduction to API provided by PyTorch datasets.

3
Loading a dataset into Colab

The procedure of loading a custom dataset into Google Colab.

4
Building a PyTorch dataset

The procedure of building a custom PyTorch dataset.

5
Image augmentation fundamentals

Introduction to image augmentation methods.

6
Image augmentation in PyTorch

How to utilize PyTorch’s API to facilitate image augmentation.

Recognizing handwritten digits

1
Downloading the dataset

How to import in the dataset used for this project.

2
Understanding the dataset

Exploration of various characteristics of the dataset.

3
Implementing a starting solution

The design of a starting neural network to recognize handwritten digits.

4
Training and evaluating

The process of training and evaluating deep learning models.

5
Choosing the size of input and output layers

The intuition behind choosing the size of input and output layers of a neural network.

6
Choosing the size of hidden layers

The intuition behind choosing the size of hidden layers of a neural network.

7
Loss functions

Discussion regarding and comparisons between loss functions.

8
Activation functions and weight initialization

Discussion regarding activation functions and weight initialization to avoid the vanishing gradient problem.

9
Optimizers

Discussion regarding and comparisons between optimizers.

Transfer learning for object recognition

1
Downloading the dataset

How to import in the dataset used for this project.

2
Understanding the dataset

Exploration of various characteristics of the dataset.

3
What is transfer learning?

Explanation of the practice of transfer learning.

4
The transfer learning workflow

Detailed procedure of a transfer learning workflow.

5
Training and evaluating

The process of training and evaluating deep learning models.

6
Pretrained models for transfer learning

Discussion regarding and comparisons between pretrained models provided in PyTorch.

Recognizing fashion items

1
Downloading the dataset

How to import in the dataset used for this project.

2
Understanding the dataset

Exploration of various characteristics of the dataset.

3
Convolutional network fundamentals

Introduction of convolutional neural networks and the problems they try to address.

4
Implementation in PyTorch

The procedure of implementing a convolutional neural network in PyTorch.

5
Residual network fundamentals

Introduction of residual neural networks and the problems they try to address.

6
Residual blocks in convolutional networks

Discussions regarding and comparisons between different residual blocks.

7
Implementation in PyTorch

The procedure of implementing a residual neural network in PyTorch.

Deep learning best practices

1
General ensembling in machine learning

Introduction to the concept of ensembling in general machine learning.

2
Ensembling in deep learning

Unique methods of ensembling in deep learning with neural networks.

3
Data versioning

Introduction to data version in deep learning and why it is important.

4
Reproducibility

Introduction to reproducibility in deep learning and why it is important.

5
When not to use deep learning

Discussions of various situations where deep learning is not desirable.

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