What are Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs) generate data that mimics a given dataset in a min-max game between a generator and discriminator.
Tag: machine learning
How do Recurrent Neural Networks (RNNs) Work
Recurrent Neural Networks (RNNs) can handle variable-length sequential data, making them ideal for natural language and audio tasks.
About Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNNs) in machine learning have transformed computer vision by learning spatial features from input data.
Understanding Fully Connected Neural Networks
Fully connected neural networks remain one of the essential building blocks for many of the state of the art systems today.
How to Load Images in Machine Learning Models
TensorFlow makes loading images in machine learning easier and properly handles image data, which leads to better trained models.
How to Load Text into Machine Learning Model
Loading text data into machine learning models made easy with TensorFlow. Convert text data into numerical representations and preprocess it.
A Guide to Anomaly Detection in Machine Learning
Anomaly Detection: Identifying unusual patterns in data. Techniques include Gaussian Distribution, LOF & Clustering-based methods.
Dimensionality Reduction: Simplifying Complex Data
Purpose of dimensionality reduction is simplifying complex data & improving performance in unsupervised learning.
What is Clustering: Grouping Data in Machine Learning
Clustering works by grouping similar data points together for valuable insights in unsupervised Machine Learning.
Regression in Machine Learning: What is it?
Learn about various regression algorithms and its applications with our comprehensive guide on regression in machine learning
Reinforcement Learning: How Does It Work?
Unlock the full potential of AI with deep reinforcement learning - a powerful technique for game playing, robotics, autonomous vehicles...
Master the Art of Semi Supervised Learning
Discover the power of semi-supervised learning: a technique that improves accuracy and performance while using less labeled data.