Skip to content SudoAll About LSTM Networks Explained Posted on March 18, 2025March 3, 2026 by David Saliba Introduction: The Power of LSTMs The Long Short-Term Memory (LSTM) network is a specialised kind of Recurrent Neural Network (RNN) architecture, designed specifically to solve the problem of vanishing gradients that plagues traditional RNNs when dealing with long sequences of data. While standard RNNs struggle to retain information from many steps ago, LSTMs are engineered with a dedicated Cell State

Siamese network is an artificial neural network that is used to find out how similar two objects are when comapring them with each other

Explaining what an artificial neural network is, how it works, and what it does through its major characteristics

Pruning methods have shown to be effective at reducing the size of deep neural networks while keeping accuracy almost intact. Among the most effective methods are those that prune a network while training it with a sparsity prior loss and learnable dropout parameters. A shortcoming of these approaches however is that neither the size nor the inference speed of the pruned network can be controlled directly; yet this is a key feature for targeting deployment of CNNs on low-power hardware. To overcome this, we

Artificial Intelligence's research and new models with more advance architectures is evolving very rapidly. But at the same time research dynamics are shifting towards analyzing and understanding the more hidden bugs in training neural networks. The

Discover how to implement a c++ neural network with ease. This guide breaks down key concepts and offers practical examples for quick mastery

Alignment Research Center --> [2] We are interested in estimating the probability that our model \(M\) behaves catastrophically on a particular distribution of inputs [3] : \(\mathbb P_{x \sim D}(C(M(x)))\) Deficiencies of Adversarial Training To reduce the risk of our AI system behaving catastrophically, we could use adversarial training: having a red team attempt to find catastrophic inputs and training \(M\) until those inputs are no longer catastrophic. However, such systems can still behave catastrophi

Neural networks have really taken over for solving image recognition and high sample rate data problems in the last couple of years. In all honesty, I promise I won’t be teaching you what neural networks are or CNN’s are. There are hundred’s of resources that are published everyday explaining them. I’ll post few links below.... Continue Reading

Boost your neural network model performance and avoid the inconvenience of overfitting with these key regularization strategies. Understand how L1 and L2, dropout, batch normalization, and early stopping regularization can help

In recent times, deep neural networks achieved outstanding predictive performance on various classification and pattern recognition tasks. However, many re