MP4 | Video: h264, 1280x720 | Audio: AAC, 48 KHz, 2 Ch

Genre: eLearning | Language: English + .srt | Duration: 61 lectures (5 hours, 43 mins) | Size: 2.8 GB

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