We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

AI methods are increasingly being used to improve grid reliability. Physics-informed neural networks are highlighted as a ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, innovatively launches a quantum-enhanced deep convolutional neural network image 3D reconstruction ...

RMSprop Optimizer Explained in Detail. RMSprop Optimizer is a technique that reduces the time taken to train a model in Deep Learning. The path of learning in mini-batch gradient descent is zig-zag, ...

Overview: In 2025, Java is expected to be a solid AI and machine-learning language.Best Java libraries for AI in 2025 can ease building neural networks, predict ...

Even networks long considered "untrainable" can learn effectively with a bit of a helping hand. Researchers at MIT's Computer ...

Digital Twin of the Ocean is a continuously updated virtual counterpart of the real ocean that exchanges data in real time ...

Machine learning techniques that make use of tensor networks could manipulate data more efficiently and help open the black ...

Machine learning and deep learning are both parts of artificial intelligence, but they work in different ways — like a smart student versus a super-specialised ...

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, launched a hybrid quantum neural network structure (H-QNN) ...

Overview:  Reinforcement learning in 2025 is more practical than ever, with Python libraries evolving to support real-world simulations, robotics, and deci ...