Complex Deep Learning concepts explained in a straightforward way.
Concepts explained in a thought provoking fashion to strengthen understanding.
Visuals to simplify understanding of core concepts in Deep Learning.
Get started with the world of deep learning with our in depth articles explaining all the core concepts made for people of all levels. Our guided set of articles guarantees a comprehensive understanding of deep learning fundamentals.
Most people focus on how deep learning works, but it's also important to understand why. Our articles explain both the working principles of deep learning as well as the intuition behind how exactly they work.
With articles packed with illustrations and examples for each concept, never fail to understand how exactly a certain deep learning mechanism works under the hood because you can visualize it's inner parts.
Neural networks are the essential building blocks of deep learning. Explore how exactly neural networks are defined in this lesson.
Learn how exactly neural networks learn from some given information which we provide to them with the backpropagation algorithm.
Loss functions allow neural networks to penalize itself effectively while training on some data. Learn how some of the most popular loss functions work in this article.
Optimizers are a set of algorithms which allow the deep learning models to reduce their loss efficiently and effectively. Understand how they work in this article.
Generalization represents the techniques we apply to deep learning models during training to ensure they learn from the data in the most effective way.
Word Vectors are the most popular way to represent textual data in numeric format. Understand how we go about this using Google's Word2Vec algorithm.
RNNs are the entry point to using Deep Learning for Natural Language Processing. Learn exactly how they work as sequential models.
Convolutional Neural Networks (CNNs) allow us to use Deep Learning to train models which take visual data as input. Learn how they work.
Tutorial on how to build deep learning models using TensorFlow 2's tf.keras API.
