April 2018
Intermediate to advanced
4h 42m
English
Overview
In this 4 hr course, you'll dive into the PyTorch library to unlock the potential of deep learning frameworks using hands-on projects. You'll learn how to create neural networks to tackle machine learning tasks for spatial, sequential, and structured data.
What I will be able to do after this course
- Master the PyTorch library for building and training deep learning models.
- Design and train convolutional neural networks for image processing tasks.
- Develop recurrent neural networks for sequence modeling applications.
- Gain insights into using autoencoders for data compression and noise reduction.
- Implement reinforcement learning techniques in solving real-world problems.
Course Instructor(s)
Anand Saha, a seasoned data scientist and educator, brings years of experience in developing machine learning and deep learning solutions. Anand has authored multiple courses and tutorials, focusing on simplifying complex concepts for learners. With a passion for practical applications, Anand aims to empower students by combining theoretical knowledge with coding experience.
Who is it for?
This course is ideal for Python programmers familiar with basic mathematics and machine learning principles. It's tailored for individuals eager to master PyTorch and deep learning techniques. Whether you're beginning your deep learning journey or expanding your current skill set, this course offers valuable insights and practical tools.
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