ml
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How Can Products Study Their Users Better by Using New Tech? (AI/ML)
As the world of technology continues to progress, the trend of personalised products is becoming increasingly prevalent. To serve users better, products should strive to comprehend their target audience’s distinct requirements and choices....
Atrina Technologies · 04 August 2023 · 83
Importance of data annotation services for AI and machine learning applications
Introduction to Data Annotation Services The success of artificial intelligence (AI) and machine learning (c) applications depends largely on the availability of accurate and relevant data. However, raw data is often unstructured and lacks the conte...
Alex · 1 year ago · 55
How Data Labeling Can Help Improve Your Machine Learning Models
Machine learning models are becoming increasingly popular as part of many applications, from facial recognition to autonomous driving. Although these models are powerful tools, they can only be as accurate as the data used to train them. Data labelin...
Alex · 23 March 2023 · 107
Top Artificial Intelligence Trends Businesses Must Watch Out for in 2023
Artificial Intelligence (AI) is a profound technology, as it allows machines, especially computer systems, to mimic the behavior of humans. Although artificial intelligence is increasingly being used in all sorts of industries to assist with repetiti...
Alex · 15 February 2023 · 126
The Benefits of Implementing Machine Learning in Your Business
It is a jungle out there, isn’t it? The competition is massive, and business and customer demands are ever-evolving. Thus, standing out, becoming a confident player, and a strong competitor for mature companies in the market is a matter of survival....
Alex · 06 January 2023 · 154 · 1
Machine Learning Foundations: Part 10 - Using NLP to build a sarcasm classifier
Previous: Part 9 - Using the Sequencing APIs Over the last few parts, we haven't done much machine learning. Instead, we looked at how you can preprocess text data to get it ready for training machine learning models. In this part, you're going to p...
Alex · 25 June 2020 · 371
Machine Learning Foundations: Part 9 - Using the Sequencing APIs
In part 8: Introduction to Natural Language Processing, we looked at how you can tokenize words with simple APIs. This allowed you to turn words into numbers or tokens so that they can be more easily represented in a computer's memory. It's the first...
Alex · 23 June 2020 · 61
Machine Learning Foundations: Part 8 - Tokenization for Natural Language Processing
Previous Part 7 - Image augmentation and overfitting Up to now, you've learned how machine learning works and explored examples in computer vision by doing image classification, including understanding concepts such as convolutional neural networks f...
Alex · 18 June 2020 · 122
Machine Learning Foundations: Part 7 - Image augmentation and overfitting
Previous part 6 - Convolutional cats and dogs Over the last few articles, you've looked at convolutional neural networks and how they can be used for computer vision. You built classifiers for fashion, horses and humans and cats and dogs. But one the...
Alex · 08 June 2020 · 39
Machine Learning Foundations: Part 6 - Convolutional cats and dogs
Previous Part 5: Classifying real-world images In this part where we'll take what you've learned about convolutional neural networks in the previous few parts and apply them to a computer vision scenario that was a Kaggle challenge not that long ago...
Alex · 04 June 2020 · 60
Machine Learning Foundations: Part 5 - Classifying real-world images
In this article we're going to look at how to use convolutional neural networks to classify complex features. In previsous part 4 - Coding with Convolutional Neural Networks, you took what you had learned about CNNS. And you saw how to improve the f...
Alex · 31 May 2020 · 49
Machine Learning Foundations: Part 4 - Coding with Convolutional Neural Networks
In the previous part 3 - Convolutions and pooling, you learned all about convolutions and how they can use filters to extract information from images. You also saw how to create pools that can reduce and compress your images without losing the vital...
Alex · 27 May 2020 · 58
Machine Learning Foundations: Part 3 - Convolutions and pooling
In the previous part 2 - First steps in computer vision, you built a neural network that could recognize items of clothing. Now that you've looked at fashion example for computer vision, you've probably noticed a big limitation for computer vision...
Alex · 26 May 2020 · 81
Machine Learning Foundations: Part 2 - First steps in computer vision
In the previous article Part 1 - What is ML?, you got an introduction to machine learning, and you saw how it works from a programmer's perspective by having you create answers and data, and letting a computer infer the rules that determine them. Thi...
Alex · 23 May 2020 · 72
Introduction to JAX (AI Adventures)
NumPy is fast, but how can we make it even faster? In this article, we're going to look at a new library from Google Research called JAX and see how it can speed up machine learning. JAX can automatically differentiate native Python and NumPy...
Alex · 16 May 2020 · 155
Machine Learning Foundations: Part 1 - What is ML?
Welcome to this series on Machine Learning Foundations. It's a course where you'll learn the fundamentals of building machine learning models using TensorFlow. The only thing that you'll need to know is a little bit of Python. So if you've tried to l...
Alex · 15 May 2020 · 128