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Course
- AI
- Data
Practical Guide to Neural Network Training: Working with Leading Frameworks
Today’s deep learning frameworks make it easier than ever to work with neural networks. This course will teach you how to efficiently build and train a neural network, while evaluating and addressing some common challenges.
What you'll learn
Machine learning is the secret behind today’s most innovative applications. The ability to build and fine-tune neural networks has become an indispensable skill in this new age of artificial intelligence.
In this course, Practical Guide to Neural Network Training: Working with Leading Frameworks, you'll gain the ability to train neural networks effectively and efficiently.
First, you'll explore popular deep learning frameworks, such as TensorFlow and PyTorch, getting hands-on experience with PyTorch and using it to preprocess data, build a neural network, and then train the model.
Next, you’ll discover how to monitor the training progress and evaluate the performance of the neural network using a validation dataset.
Finally, you’ll learn common challenges in training neural networks -- such overfitting/underfitting and vanishing/exploding gradients -- and learn strategies to balance these issues.
When you're finished with this course, you'll have the skills and knowledge needed to confidently build and train your own neural networks using popular frameworks.
Table of contents
About the author
Amber has been a software developer and technical trainer since the early 2000s. She holds certifications for AWS and a variety of Microsoft technologies. She also focuses on user experience and professional skills training, bridging the gap between techies and non-techies.
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