Deep Neural Network

A deep neural network improves over time by learning from its mistakes. When it makes a prediction like identifying a customer issue or recommending a product it checks if it was right. If it wasn't, the system adjusts itself to improve next time.

Learn what a deep neural network is, how it works, and why it is driving advancements in computer vision, natural language processing, and speech synthesis. This article provides an in-depth guide to deep neural networks, their history, layers, activation functions, and weights.

A deep neural network is a neural network with more than two layers. Deep neural networks consist of an input layer, multiple hidden layers, and an output layer. Many companies use deep neural networks due to their highly accurate decision-making capabilities.

Learn what a deep neural network is, how it evolved from machine learning and artificial neural networks, and what are the pros and cons of using it. Find out how deep nets improve accuracy but also create a black box problem for explainability.

Deep neural networks are the state-of-the-art models for understanding the content of images and videos. However, implementing deep neural networks in embedded systems is a challenging task, e.g., a typical deep neural network can exhaust gigabytes of memory and result in bandwidth and computational bottlenecks.

Learn what deep neural networks are, why they are important, and how to build one from scratch using TensorFlow and Keras. Explore the differences between ANN and DNN, and the popular deep learning tools and libraries.

Learn about neural networks, a biologically-inspired programming paradigm that enables a computer to learn from data, and deep learning, a powerful set of techniques for neural networks. This book covers core concepts, exercises, examples, and applications of neural networks and deep learning.

In a deep neural network the input layer receives data which passes through hidden layers that transform the data using nonlinear functions. The final output layer generates the model's prediction. For more details on neural networks refer to this article What is a Neural Network? Fully Connected Deep Neural Network

Deep learning is a subset of machine learning that uses neural networks with multiple layers to perform tasks such as classification, regression, and representation learning. Learn about the origins, methods, architectures, and applications of deep learning from this comprehensive Wikipedia article.

Learn the history and basic concepts of deep learning neural networks, inspired by the human brain. Understand how neurons, activation functions, and layers work together to create powerful models.