Deep Learning with PyTorch / Saha, Anand.

Format:

Video

Author/Creator:

Saha, Anand, author.

Language:

English

Subjects (All):

Neural networks (Computer science).

Python (Computer program language).

Artificial intelligence.

Machine learning.

Genre:

Electronic videos.

Physical Description:

1 online resource (1 video file, approximately 4 hr., 43 min.)

Edition:

1st edition

Place of Publication:

Packt Publishing, 2018.

System Details:

video file

Summary:

Build useful and effective deep learning models with the PyTorch Deep Learning framework About This Video Explore PyTorch and the impact it has made on Deep Learning Design and implement powerful neural networks to solve some impressive problems in a step-by-step manner Follow the examples to solve similar use cases outside this course In Detail This video course will get you up-and-running with one of the most cutting-edge deep learning libraries: PyTorch. Written in Python, PyTorch is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks. By the end of the video course, you will have developed a good understanding of, and feeling for, the algorithms and techniques used. You'll have a good knowledge of how PyTorch works and how you can use it in to solve your daily machine learning problems. This course uses Python 3.6, and PyTorch 0.3, while not the latest version available, it provides relevant and informative content for legacy users of Python , and PyTorch.

Participant:

Presenter, Anand Saha.

Notes:

Title from title screen (viewed July 11, 2018).

Date of publication from resource description page.

Online resource; Title from title screen (viewed April 30, 2018)

OCLC:

1043906469