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Explainable AI is possible
The complexity of AI, particularly deep learning models, has led to the "black box" criticism, highlighting the lack of understanding about how deep learning models arrive at their decisions. While there's truth to this concern, having a nuanced view is important. In this post, I share my view on AI explainability, that it is complex, however possible.
Generate Music with AI
In this post, we will get into music generation with AI. We will briefly explore existing AI applications generating audio. We will explore transformer usage while coding music generation with HuggingFace transformers in Python.
TensorFlow: Transfer Learning (Feature Extraction) in Image Classification
Image classification is a complex task. However, we can approach the problem while reusing state-of-the-art pre-trained models. Using previously learned patterns from other models is named "Transfer Learning." This way, we can efficiently apply well-tested models, potentially leading to excellent performance.
TensorFlow: Convolutional Neural Networks for Image Classification
In this post, I have demonstrated CNN usage for birds recognition using TensorFlow and Kaggle 400 birds species dataset. We observed how the model works with the original and augmented images.
Deep Learning with DataCamp and Twitter
While having some machine learning experience of working with Scikit Learn, I was always interested in Deep Learning. The plan is to learn basic concepts and apply algorithms to a real-life situation, which I have always liked.
Deep Learning vs Machine Learning
Artificial Intelligence (AI) is a field of computer science. AI provides methods and algorithms to mimic human intelligence, reasoning, and decision-making and provide insights, which businesses could use in research or industry to build new exciting and innovative products or services. Machine Learning (ML) is a subset of AI with algorithms that learn from data. In this post, we sort out the differences between AI and ML.