balderdashblog

The future belongs to those who are able to lead and 'speak data'. This article highlights the need for organizations to embrace data literacy and create a culture that values data-driven decision-making. As we move from slide deck to value, I think three things are essential: collecting meaningful data, using data meaningfully and creating meaningful trust in data-driven decisions.

At the start of a second Trump term in America's highest office, it is important that all minorities honor the ongoing struggle for our liberties by taking up space. We have to gaurd against the erosion of our liberties and the diversity initiatives that embody these liberties; we need renewed activism to protect and advance social progress.

I've been thinking about theory U: who I am and what is my work to do.

I've been thinking about theory U: who I am and what is my work to do.

Discovering your core values is an essential step toward living a more meaningful and intentional life. This post explores the importance of self-awareness, the practice of depth analysis, and how working with a coach can help uncover your true motivations. By aligning your actions with your values—such as authenticity, civic-mindedness, spiritual purpose, humble mastery, and balance—you can cultivate a life of clarity, fulfillment, and impact.

I've been thinking about the mental health crisis, and what an intervention at the root of the problem might look like.

Explore the transformative power of coaching, a forward-focused practice designed to enhance emotional and spiritual intelligence, foster self-discovery, and build capacity for meaningful action. This post delves into the core principles of coaching, its role in personal and spiritual development, and real-world examples of coaching inquiries.

The duty we owe to our personal development is to honestly know who we are and how we pitch up for ourselves in every moment. It is for this reason that I have integrated this inquiry into my mindfulness practice. Putting three questions to myself several times throughout the day has accelerated my personal development, and I offer them to you as a development tool.

According to Patric Lencioni (author of Five dysfunctions of a Team), organizations fail to achieve teamwork because they unknowingly fall prey to the five natural but dangerous pitfalls: lack of trust, fear of confrontation, lack of commitment, lack of accountability, and lack of focus on goals. If one of these dysfunctions is present in a team, it results in poor team performance.

Thanks for visiting the Hello World post for my new blog. I created this blog primarily as a means to develop my creative self-expression. I suspect it will reflect my journey as a seeker of Truth and my perspective on Truth.

Discover how organisational and social design create adaptive frameworks that align tasks, foster collaboration, and empower people to navigate complexity and achieve sustainable success.

Cross-validation is a method for evaluating hypothesis functions by dividing data into segments for training and testing. Techniques like k-fold cross-validation estimate empirical loss, guiding model selection by minimizing prediction errors. This avoids bias and ensures robust model evaluation.

How well is your model performing? How do you know when one model statistically outperforms another? This articles offers some answers to these question.

The k-nearest neighbour (k-NN) algorithm is a simple yet powerful classification technique that determines the class of an unlabelled instance based on the proximity of its k nearest neighbours in an multi-dimensional feature space. The classification relies on a distance measure, with labels assigned by a majority vote or distance-weighted vote.

Supervised learning aims to approximate the true function underlying data by optimizing model parameters and hyper-parameters. Techniques like manual, grid, and random searches select hyper-parameters, with random search often being more efficient. This helps minimize empirical loss and improve model performance.

Data mining is the process of applying machine learning techniques to identify patterns in the relationships between data elements, as part of the broader knowledge discovery process. A common framework for this process involves six steps: understanding the problem and objectives, identifying and acquiring data, preparing the data for analysis, applying machine learning techniques, evaluating the usefulness of the discovered patterns, and deploying the knowledge for practical use. This process may include several feedback loops to refine the results as needed.

Neural networks are inspired by the way biological neurons in the human brain process and transmit information. Each neuron adjusts its sensitivity to inputs, contributing to the brain's overall computational power. Artificial neural networks, particularly models like the perceptron and multilayer perceptron (MLP), replicate this process. In an MLP, nodes are organized into layers—input, hidden, and output—where each node computes weighted inputs to produce outputs. The network's predictive power relies on the specific weighting of connections between nodes, enabling it to approximate any continuous function. Training the network, especially using the backpropagation algorithm, is essential for optimizing these weights, allowing the network to learn from data effectively. This process of learning through weight adjustment is what gives neural networks their ability to perform complex tasks and make accurate predictions.

Machine learning is the scientific discipline concerned with the design and development of algorithms that allow computers to infer knowledge from input data. This post explores supervised and specifically the classification sub problem.