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Feature preprocessing


Elena Daehnhardt ·

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...

TensorFlow: Evaluating the Regression Model


Elena Daehnhardt ·

In this post, we have performed the evaluation of four regression models using TensorFlow. MAE and MSE error metrics were used to compare the Sequential models while finding the best neural network architecture regarding the defined hyperparameters. Read more...

TensorFlow: Regression Model


Elena Daehnhardt ·

I have described regression modeling in TensorFlow. We have predicted a numerical value and adjusted hyperparameters to better model performance with a simple neural network. We generated a dataset, demonstrated a simple data split into training and testing sets, visualised our data and the created neural network, evaluated our model using a testing dataset. Read more...

TensorFlow: Global and Operation-level Seeds


Elena Daehnhardt ·

In training Machine Learning models, we want to avoid any ordering biases in the data. In some cases, such as in Cross-Validation experiments, it is essential to mix data and ensure that the order of data is the same between different runs or system restarts. We can use operation-level and global seeds to achieve the reproducibility of results. Read more...

Tensors in TensorFlow


Elena Daehnhardt ·

TensorFlow is a free OS library for machine learning created by Google Brain. Tensorflow has excellent functionality for building deep neural networks. I have chosen TensorFlow because it is pretty robust, efficient, and can be used with Python. In this post, I am going to write about how we can create tensors, shuffle them, index them, get information about tensors with simple examples. Read more...

GitHub Codespaces


Elena Daehnhardt ·

GitHub codespaces provide a development environment running in the cloud. A codespace environment is created with the help of configuration files added to a GitHub repository. Read more...

TensorFlow on M1


Elena Daehnhardt ·

TensorFlow is a free OS library for machine learning created by Google Brain. Tensorflow has excellent functionality for building deep neural networks. I have chosen TensorFlow because it is pretty robust, efficient, and can be used with Python. Since I like Jupyter Notebooks and Conda, they were also installed on my system. Next, I am going through simple steps to install TensorFlow and the packages above on M1 macOS Monterey. Read more...

Mining Microblogs for Culture-awareness in Web Adaptation


Elena Daehnhardt ·

In this post, I am briefly writing up about what I did in my PhD research at Heriot-Watt University and the main idea behind the thesis. Read more...

Artificial Neural Networks


Elena Daehnhardt ·

Artificial neural networks (ANNs) are the cornerstone of Deep Learning algorithms. The name and the architecture are adopted from the human brain's neural network. ANNs are designed to simulate human reasoning based on how neurons communicate. ANNs contain a set of artificial neurons connected. Read more...

Minimalism in Coding and Design


Elena Daehnhardt ·

Nowadays, technology advances so rapidly that I sometimes feel like running after leaving the train. More technical knowledge is needed every day. Yesterday, it was GIT and workflows, and today it is Docker. What is next? Read more...

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