Deep Learning With Google Colab

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 foldersUnderstand the general workflow of a deep learning projectExamine the various APIs (datasets, modeling, training) PyTorch offers to facilitate deep learningLearn about the theoretical basis for various deep learning models such as convolutional networks or residual networks and what problems they addressGain an overview understanding of deep learning in the context of the artificial intelligence field and its best practices

Overview

Section 1: Getting started in Google Colab

Lecture 1 Introduction

Lecture 2 Registering for a Google account

Lecture 3 Navigating to Google Colab

Lecture 4 Exploring your Google Colab Notebook

Lecture 5 The definition of notebooks

Lecture 6 Running your first Google Colab code cell

Lecture 7 The markup language Markdown

Lecture 8 Writing Markdown in Google Colab

Lecture 9 Writing LaTeX in Google Colab

Lecture 10 Section conclusion

Section 2: The ecosystem of Google Colab

Lecture 11 Installing packages in Google Colab

Lecture 12 Working with files using Google Drive

Lecture 13 Working with files directly in Google Colab

Lecture 14 Sharing files via Google Drive

Lecture 15 Introduction to version control with Git and GitHub

Lecture 16 Sending Google Colab notebooks to GitHub

Section 3: Introduction to PyTorch

Lecture 17 Creating a tensor

Lecture 18 Tensor operations

Lecture 19 GPUs in the context of deep learning

Lecture 20 Turning on your Colab GPU

Lecture 21 Limits of the Colab GPU

Lecture 22 Neural network basics

Lecture 23 Gradients and backpropagation

Lecture 24 Automatic differentiation in PyTorch

Lecture 25 Training a model

Lecture 26 Saving and loading models

Lecture 27 Problem statement and setup

Lecture 28 Approaches and solutions

Section 4: Working with datasets

Lecture 29 Downloading a built-in dataset

Lecture 30 Working with PyTorch datasets

Lecture 31 Loading a dataset into Colab

Lecture 32 Building a PyTorch dataset

Lecture 33 Image augmentation fundamentals

Lecture 34 Image augmentation in PyTorch

Section 5: Recognizing handwritten digits

Lecture 35 Downloading the dataset

Lecture 36 Understanding the dataset

Lecture 37 Implementing a starting solution

Lecture 38 Training and evaluating

Lecture 39 Choosing the size of input and output layers

Lecture 40 Choosing the size of hidden layers

Lecture 41 Loss functions

Lecture 42 Activation functions and weight initialization

Lecture 43 Optimizers

Section 6: Transfer learning for object recognition

Lecture 44 Downloading the dataset

Lecture 45 Understanding the dataset

Lecture 46 What is transfer learning?

Lecture 47 The transfer learning workflow

Lecture 48 Training and evaluating

Lecture 49 Pretrained models for transfer learning

Section 7: Recognizing fashion items

Lecture 50 Downloading the dataset

Lecture 51 Understanding the dataset

Lecture 52 Convolutional network fundamentals

Lecture 53 Implementation in PyTorch

Lecture 54 Residual network fundamentals

Lecture 55 Residual blocks in convolutional networks

Lecture 56 Implementation in PyTorch

Section 8: Deep learning best practices

Lecture 57 General ensembling in machine learning

Lecture 58 Ensembling in deep learning

Lecture 59 Data versioning

Lecture 60 Reproducibility

Lecture 61 When not to use deep learning

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