AIHUB

Introduction DNNs, which stand for Deep Neural Networks, are the driving force behind state-of-the-art Artificial Intelligence and Deep Learning. By stacking several layers on top of each other, i.e., one working as a filter or a feature extractor, they can learn complex patterns from information such as images, voice, and text. In contrast to simple neural networks, DNNs extract representation hierarchies, allowing them to represent nuanced differences and abstract ideas. This layered depth provides them with better performance in applications such as self-driving, medical diagnosis, natural language processing, and more. In this blog, you will learn: What constitutes a neural network as “deep” Standard architectures include deep feedforward, CNN, RNN, Transformer, and Autoencoders. Key training methods may include optimization, regularization, and hyperparameter tuning. Real-world examples of impactful transformative effects. How to implement and train your own DNN best practic...

Neural Networks Neural Networks are the foundation of advanced artificial intelligence. They are the building blocks that made deep learning possible and catalyzed revolutionary change in numerous industries. At their core, neural networks are brain-inspired computational models. These models are comprised of nodes—or “Neurons”—which are interconnected in layers. Every neuron takes input, applies weights and biases, utilizes an activation function, and sends output to the subsequent layer. Neural networks learn by modifying these weights based on the data they process, using training and feedback mechanisms. Whether you are building a recommendation system, or chatbot, or detecting diseases in medical images, in all these scenarios, neural networks play a vital role. They can model complex, nonlinear relationships between input and output, making them ideal for tasks such as image and speech recognition, natural language processing, and many more. In this blog, we will explore ho...

Introduction In the constantly changing world of Artificial Intelligence , Deep Learning is a revolutionary branch of Machine Learning . By replicating the architecture and operation of the human brain, deep learning allows machines to handle and interpret data with breathtaking precision. From virtual assistants to autonomous vehicles, deep learning software is behind some of the most exciting innovations in technology today. In this comprehensible blog, we will study what deep learning is, how it works, why it is so important, and its real-world applications. What is Deep Learning? Deep Learning is a form of Machine Learning that utilizes Artificial Neural Networks with multiple layers, hence the name ‘deep’. These networks are capable of learning representations from data autonomously, and as such, as very powerful in applications like image recognition, speech translation, natural language processing, and much more. In contrast to classical machine learning, where data scie...

Do you know how human brains work? Have you ever wondered if animals also have a brain but can’t make decisions like humans? Why so? Humans do all this with the help of intelligence, so when a human puts intelligence into a machine, it is called artificial intelligence. The term artificial intelligence was first coined decades ago in 1956 by John McCarthy at the Dartmouth Conference. he defined artificial intelligence as “Artificial Intelligence is the science and engineering of making intelligent machines”. AI is the technique of getting machines to work just like humans. These machines are artificially incorporated with human-like intelligence to perform tasks as we do. This intelligence is built using complex algorithms and functions. The practical applications of AI include healthcare, robotics, business analytics, and marketing. AI has become so general that we don’t realize we use it in our daily lives, including smartphones, vehicles, social media, games, banking, and many...