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2024
Types of Machine Learning Models: Explained with Examples
Explore the three main types of machine learning models—supervised, unsupervised, and reinforcement learning—with clear explanations and practical examples in TensorFlow.js.
Why Split a Dataset into Training, Validation, and Test Sets?
Learn the purpose of dividing datasets into training, validation, and test sets, and see practical examples of how to implement this in TensorFlow.js for better machine learning models.
Selecting an Appropriate Model Architecture for a Given Problem
Learn the step-by-step process of selecting the right model architecture for your machine learning problem. Understand key considerations like data type, task complexity, and TensorFlow.js examples.
The Role of Data Preprocessing in TensorFlow.js Models
Discover the importance of data preprocessing in building effective TensorFlow.js models. Learn common techniques like normalization, encoding, and handling missing values to optimize model performance.
Implementing Recurrent Neural Networks (RNNs) in TensorFlow.js Using Tabular Data
Learn how to implement Recurrent Neural Networks (RNNs) in TensorFlow.js using tabular data. This guide walks you through preprocessing, building an RNN architecture, training, and evaluation for sequential data tasks.
Understanding Gradient Descent Optimization and Its Variants in TensorFlow.js
Learn the fundamentals of gradient descent optimization, including stochastic gradient descent and other variants, in the context of TensorFlow.js. Explore how these methods impact model training.