Author Collection
Ian Goodfellow, Yoshua Bengio, and Aaron Courville
1 book with highlights
Books
Featured Highlights
Overfitting occurs when a model learns the noise in the training data instead of the underlying distribution.
From Deep Learning
Convolutional neural networks (CNNs) have proven to be very effective for image processing tasks.
From Deep Learning
Deep learning models are often seen as black boxes because it can be difficult to interpret how they make decisions.
From Deep Learning
Deep learning is a subfield of machine learning that uses algorithms inspired by the structure and function of the brain.
From Deep Learning
Regularization techniques are essential to prevent overfitting and improve the model's performance on unseen data.
From Deep Learning
The future of deep learning will likely involve more unsupervised and semi-supervised learning techniques.
From Deep Learning
The key to deep learning is representation learning, where the model learns to represent the input data in a way that makes it easier to solve the task at hand.
From Deep Learning
Transfer learning allows us to leverage knowledge gained from one task to improve learning in another task.
From Deep Learning
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data.
From Deep Learning
The success of deep learning has been driven by the availability of large datasets and powerful computation.
From Deep Learning