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Can symbolic regression be the key to transforming opaque deep learning models into interpretable, closed-form mathematical equations? or Say you have trained your deep learning model. It works. But do you know what it has actually learned? A team of University of Cambridge researchers propose ‘SymTorch’, a library designed to integrate symbolic regression (SR) into […]
If it’s about building better systems, we must ask: Better for whom? In parts one and two of this series, I’ve looked at AI engineering as the discipline that integrates technical practices and orchestrates the -Ops landscape. And I’ve addressed end-to-end accountability, shared governance, and adaptive resilience. But there’s a […] The post AI engineering:
AI engineering: The ethical and social imperatives Read More »
In this tutorial, we walk through advanced usage of Einops to express complex tensor transformations in a clear, readable, and mathematically precise way. We demonstrate how rearrange, reduce, repeat, einsum, and pack/unpack let us reshape, aggregate, and combine tensors without relying on error-prone manual dimension handling. We focus on real deep-learning patterns, such as vision
Earth observation (EO) constellations capture huge volumes of high-resolution imagery every day, but most of it never reaches the ground in time for model training. Downlink bandwidth is the main bottleneck. Images can sit on orbit for days while ground models train on partial and delayed data. Microsoft Researchers introduced ‘OrbitalBrain’ framework as a different
In this tutorial, we implement Tree-KG, an advanced hierarchical knowledge graph system that goes beyond traditional retrieval-augmented generation by combining semantic embeddings with explicit graph structure. We show how we can organize knowledge in a tree-like hierarchy that mirrors how humans learn, from broad domains to fine-grained concepts, and then reason across this structure using
At its core, ML involves algorithms that analyze data, recognize patterns, and make predictions. These models “learn” from past data to improve their performance over time. For example, an ML model trained on user purchase history can predict which products a customer might buy next. Artificial Intelligence (AI) is no longer a future concept. This is
Deep Learning vs. Machine Learning: Key Differences Explained for Business Leaders Read More »
Are you following the trend or genuinely interested in Machine Learning? Either way, you will need the right resources to TRUST, LEARN and SUCCEED. If you are unable to find the right Machine Learning resource in 2026? We are here to help. Let’s reiterate the definition of Machine Learning… Machine learning is an exciting field
50+ Machine Learning Resources for Self Study in 2026 Read More »
In this tutorial, we demonstrate a realistic data poisoning attack by manipulating labels in the CIFAR-10 dataset and observing its impact on model behavior. We construct a clean and a poisoned training pipeline side by side, using a ResNet-style convolutional network to ensure stable, comparable learning dynamics. By selectively flipping a fraction of samples from
Data science powers decision-making across modern businesses, from data preparation and automation to advanced analytics and machine learning. Learning it requires a strong foundation in mathematics, statistics, programming, and practical problem-solving. The good news is that data science can be self-learned with the right resources and consistent practice. Books remain one of the most effective
30 Best Data Science Books to Read in 2026 Read More »
There is no shortage of resources available online and offline when it comes to learning Python. However, not all Python books are created equal. Some are best suited for beginners, while others are designed for experienced programmers or learners with specific goals. In this article, I have curated the best Python books across different categories
15 Best Python Books for Beginners to Advanced Learners [2026 Edition] Read More »
