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Decision trees are widely used in machine learning problems. We'll assume you are already familiar with the concept of decision trees and you've just trained your tree based algorithm! Advice: If not, you can read our in-depth guide on "Decision Trees in Python with Scikit-Learn guide". Now, it...
Cássia Sampaio
Article
The Random Forest algorithm is one of the most flexible, powerful and widely-used algorithms for classification and regression, built as an ensemble of Decision Trees. If you aren't familiar with these - no worries, we'll cover all of these concepts. In this in-depth hands-on guide, we'll build an intuition on...
The Random Forest algorithm is a tree-based supervised learning algorithm that uses an ensemble of predictions of many decision trees, either to classify a data point or determine its approximate value. This means it can either be used for classification or regression. When applied for classification, the class of the...
Suppose you want your Keras model to have some specific behavior during training, evaluation or prediction. For instance, you might want to save your model at every training epoch. One way of doing this is using Callbacks. In general, Callbacks are functions that are called when some event happens, and...
Felipe Antunes
Object detection has been gaining steam, and improvements are being made to several approaches to solving it. In the past couple of years, YOLO-based methods have been outperforming others in terms of accuracy and speed, with recent advancements such as YOLOv7 and YOLOv6 (which was released independently, after YOLOv7). However...
David Landup
Object detection is a large field in computer vision, and one of the more important applications of computer vision "in the wild". From it, keypoint detection (oftentimes used for pose estimation) was extracted. Keypoints can be various points - parts of a face, limbs of a body, etc....
Object detection is a large field in computer vision, and one of the more important applications of computer vision "in the wild". From it, instance segmentation was extracted, and is tasked with having models predict not only the label and bounding box of an object, but also the...
The learning rate is an important hyperparameter in deep learning networks - and it directly dictates the degree to which updates to weights are performed, which are estimated to minimize some given loss function. In SGD: $$ weight_{t+1} = weight_t - lr * \frac{derror}{dweight_t} $$ With a learning...
Improving the performance of a training loop can save hours of computing time when training machine learning models. One of the ways of improving the performance of TensorFlow code is using the tf.function() decorator - a simple, one-line change that can make your functions run significantly faster. In this...
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