Blog

Introduction to Recurrent Neural Networks (RNNs) and LSTMs

Learn the fundamentals of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, and how they are applied to sequential data such as time series, language modeling, and more.

Understanding Gradient Descent and its Variants (SGD, Adam, RMSprop)

A deep dive into gradient descent and its variants (SGD, Adam, RMSprop), exploring how these optimization algorithms work and their specific use cases in training deep neural networks.

Understanding Overfitting and Underfitting in Machine Learning

Learn how to identify and prevent overfitting and underfitting in machine learning models, with practical examples, detection strategies, and techniques to strike the right balance for better generalization.

Hyperparameter Tuning in CNNs - Grid Search and Random Search

Dive into tuning convolutional neural network (CNN) hyperparameters using grid search, random search, and Keras Tuner, with practical examples and real-world use cases.

Improving CNNs with Regularization and Dropout Techniques

Explore regularization techniques such as L2 regularization, dropout, and batch normalization to improve the performance of convolutional neural networks (CNNs) and prevent overfitting.