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.
Author Name: Amber Israelsen
Author Description:
Amber has been a software developer and technical trainer for over two decades, sharing her expertise in AI, machine learning, AWS, and Power Apps with students around the world. She has a knack for making complex tech topics easy to grasp, whether you’re a developer, designer, or business professional. Amber holds certifications in machine learning, AWS, and various Microsoft technologies, including her experience as a former Microsoft Certified Trainer. With a strong background in visual com… more

Table of Contents

• Course Overview
  2mins
• Training a Basic Neural Network with PyTorch
  18mins
• Challenges: Overfitting, Underfitting, and Vanishing/Exploding Gradients
  9mins