Machine learning resources containing Deep Learning, Machine Learning and Artificial Intelligent resources. A-Z Machine learning resources to learn machine learning.
AI, ML and DL are related to each other. AI is a superset of ML and DL. What we do in the field of ML and DL all comes under AI. To better understand all of them, Let's dive in…
Affine Transformation helps to modify the geometric structure of the image, preserving parallelism of lines but not the lengths and angles. It preserves collinearity and ratios of distances. It is one type of method we can use in Machine Learning and Deep Learning for Image Processing and also for Image Augmentation.
In this notebook we will be learning how to use Transfer Learning to create the powerful convolutional neural network with a very little effort, with the help of MobileNetV2 developed by Google that has been trained on large dataset of images.
A hyperparameter is a parameter or a variable we need to set before applying a machine learning algorithm into a dataset.These parameters express "High Level" properties of the model such as its complexity or how fast it should learn. Hyperparameters are usually fixed before the actual training process begins.
In the real world, it is very difficult to explain behavior as a function of only one variable, and economics is no different.
We all love to see beautiful images, but have you ever thought how do computers see an image? In this tutorial, we will give an explanation of how images are stored in a computer.
Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.If the training has not converged, try running it for longer.
Here are 15 Best Machine Learning Course for Machine Learning. It will give you the great knowledge about Machine Learning and Deep Learning.
Deep Learning is a subfield of Machine Learning because it makes use of Deep Neural Networks inspired by the structure and function of the brain called Artificial Neural Networks.
CNN's achieve state of the art results in the variety of problem areas including Voice User Interfaces, Natural Language Processing, and Computer Vision.

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