Jethro Odeyemi
Lead Software Engineer at Roamlii and independent research scientist with interest machine learning, medical robotics, and human-computer interaction.
- Saskatoon, SK
- Roamlii
- Google Scholar
- ORCID
- ResearchGate
- Github
- Medium
- X (formerly Twitter)
You May Also Enjoy
Why Backprop Isn’t Magic: The Challenge of Local Minima
7 minute read
Published:
Backpropagation is the cornerstone algorithm powering much of the deep learning revolution. Coupled with gradient descent, it allows us to train incredibly complex neural networks on vast datasets. However, it’s not a silver bullet. One of the fundamental challenges that can prevent backpropagation from finding the best possible solution is the presence of local minima in the optimization landscape. Read more
DeepSeek R1: Pioneering Reasoning in Large Language Models Through Reinforcement Learning
3 minute read
Published:
The development of reasoning capabilities in large language models (LLMs) is a complex yet pivotal frontier in AI research. DeepSeek R1 represents a major leap in this space, introducing innovative methodologies for reasoning-oriented model training. In this post, we’ll explore what makes DeepSeek R1 significant, its architectural innovations, and its implications for the future of AI. Read more
Unveiling a $500 Billion Leap in AI: Trump’s Private Sector Investment Plan
3 minute read
Published:
President Donald Trump is set to announce a monumental private sector initiative aimed at bolstering the United States’ artificial intelligence (AI) infrastructure with an investment of up to $500 billion. This ambitious plan involves leading tech companies like OpenAI, SoftBank, and Oracle, under a collaborative venture named “Stargate.” Read more
Supervised Learning Showdown: kNN, SVM, Neural Networks, and Boosted Trees
12 minute read
Published:
In this post, we dive into the world of supervised learning, comparing the performance of four popular algorithms: k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Neural Networks (NN), and Decision Trees with Boosting (specifically, AdaBoost). We’ll analyze their effectiveness on two distinct datasets, highlighting their strengths and weaknesses. Read more