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Google DeepMind: Design And Train Neural Networks
4.9(21)101 studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate Google DeepMind course focuses on the mechanics of neural network training and optimization. Learners gain practical hands-on experience implementing multilayer perceptrons and backpropagation, diagnosing common training hurdles like overfitting, and evaluating societal safety implications.
Who It's For
Machine learning students and developers who want to master neural network training mechanics, debugging, backpropagation, and ethical risk evaluation.
Key Takeaways
- Training Mechanics & Backpropagation: Implement multilayer perceptron models and study the backpropagation algorithm in practical labs.
- Mitigating Overfitting & Underfitting: Spot and address common training issues to achieve well-generalized classification models.
- Real-World Case Studies & AI Safety: Examine real-world DeepMind applications while analyzing safety concerns and societal consequences.
About this course
In this Google DeepMind course you will focus on the training process for machine learning models. You will learn how to spot and mitigate issues when training a model, such as overfitting and underfitting. In practical coding labs, you will implement and evaluate the multilayer perceptron for simple classification tasks. This will provide insights into the mechanics of training a neural network model and the backpropagation algorithm. Research case studies will demonstrate how neural networks power real-world models. Additionally, you will consider the broader social impacts of innovation by looking beyond immediate benefits to anticipate potential risks, safety concerns, and further-reaching societal consequences.
Price
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