Artificial Intelligence

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CellStrat > Research/Blog > Artificial Intelligence
16
Sep
Generative Modeling with GANs
Posted in: Computer Vision, Deep Learning, Generative Modeling,
Tags: AI, artificial intelligence, discriminator, dl deep learning, GAN, generative adversarial network, generative modeling, generator, machine learning, ML,
10
Sep
AdaBoost (or Adaptive Boosting)
Posted in: Machine Learning,
Tags: adaboost, adaptive boosting, AI, artificial intelligence, boosting, classification, machine learning, ML,
05
Sep
Machine Learning process
Posted in: Machine Learning,
Tags: AI, artificial intelligence, data preparation, machine learning, ML, ml process, training step,
03
Sep
Gaussian Distribution (or Normal Distribution)
Posted in: Deep Learning, Machine Learning,
Tags: AI, artificial intelligence, bias, deep learning, gaussian, gaussian distribution, machine learning, ML, normal distribution, standard deviation, variance,
01
Sep
Logistic Sigmoid function
Posted in: Deep Learning, Machine Learning,
Tags: activation function, AI, ANNs, artificial intelligence, deep learning, logistic, logistic regression, logistic sigmoid, machine learning, ML, neural networks, S curve, sigmoid,
29
Aug
Reinforcement Learning
Posted in: Deep Learning, Reinforcement Learning,
Tags: AI, artificial intelligence, deep learning, Disrupt4.0, machine learning, ML, Q learning, reinforcement learning, rewards based learning,
28
Aug
Data Preparation for Machine Learning
Posted in: Machine Learning,
Tags: AI, artificial intelligence, data cleaning, data preparation, data sufficiency, datasets, Dimensionality Reduction, Disrupt4.0, feature engineering, feature extraction, feature scaling, feature selection, machine learning, ML,
25
Aug
Polynomial Regression
Posted in: Machine Learning,
Tags: AI, line fit, linear regression, machine learning, ML, Polynomial Regression, Regression,
20
Aug
Clustering for Half Moons dataset
Posted in: Machine Learning,
Tags: agglomerative clustering, AI, artificial intelligence, Clustering, density-based clustering, half moons, half moons dataset, k means, machine learning, ML, Unsupervised learning,
18
Aug
Convergence in a Neural Network

Convergence refers to the point in machine learning training where ideal weights have been discovered and cost function minimized. But this is, at times, plagued by issues of local minima and slow learning. Here Mini-batch gradient descent comes handy, which combines the advantages of batch gradient learning as well as stochastic gradient descent. The process […]

Posted in: Deep Learning, Machine Learning,
Tags: AI, ANN, artificial intelligence, batch gradient descent, convergence, cost minima, deep learning, global minima, learning rate, local minima, machine learning, mini-batch gradient descent, ML, neural networks, stochastic gradient descent,