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TensorFlow: Evaluating the Saved Bird Species Prediction Model


In this post, I have described the process of in-depth model evaluation. I have reused the previously created EffecientNetB0 model, which is fine-tuned with the 400 Bird Species Kaggle dataset. As a result, I have found out which bird species are not well predicted. Read more...

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. Read more...

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. Read more...

Feature preprocessing


Machine Learning algorithms often require that data is in a specific type. For instance, we can use only numerical data. In other cases, ML algorithms would perform better or converge faster when we preprocess data before training the model. Since we do this step before training the model, we call it preprocessing. Read more...

Tools and Data to Experiment with Machine Learning


Python open-source library scikit-learn provides a comprehensive selection of machine learning techniques (regression, classification, clustering), feature selection, metrics, preprocessing, and other functionality. Scikit-learn has some basic neural-network support through MLPClassifier, but for real deep learning you are better off reaching for TensorFlow's Keras API rather than the now-defunct Scikit Flow wrapper. Read more...