Dr. Jon Krohn

Unofficial, machine-generated summaries, as of 2026-11-09.

↗YOLO-NAS: Optimal Object Detection Model Architecture
↗694: CatBoost: Powerful, efficient ML for large tabular datasets — with Jon Krohn (@JonKrohnLearns)
↗Object Detection and YOLO (You Only Look Once) Architectures
↗What is Machine Vision?
↗693: YOLO-NAS: The State of the Art in Machine Vision — with Harpreet Sahota
↗AWS Neuron: The Deep Learning-Accelerating SDK
↗The Trainium Ultra Cluster (for Large Language Models)
↗Trainium and Inferentia: AWS's A.I. Accelerators
↗692: Lossless LLM Weight Compression: Run Huge Models on a Single GPU — with Jon Krohn
↗GPUs vs TPUs vs IPUs
↗CPUs vs GPUs
↗691: A.I. Accelerators: Hardware Specialized for Deep Learning — with Ron Diamant
↗Phoenix: Open-source Library for Comparing Embeddings
↗690: How to Catch and Fix Harmful Generative A.I. Outputs — with Krishna Gade
↗ML Observability vs ML Monitoring: What's the difference?
↗Drift: What It Is, Types of Drift and Why It Should Be Monitored
↗689: Observing LLMs in Production to Automatically Catch Issues — with Amber Roberts and Xander Song
↗GANs: Generative Adversarial Networks
↗How World Models Blend Generative AI with Reinforcement Learning
↗What CLIP models are (Contrastive Language-Image Pre-training)
↗688: Six Reasons Why Building LLM Products Is Tricky — with Jon Krohn (@JonKrohnLearns)
↗Autoencoders Explained
↗Generative AI for Music
↗687: Generative Deep Learning — with David Foster
↗Why All Data Scientists Should Write (in Public)
↗686: Open-Source "Responsible A.I." Tools — with Ruth Yakubu
↗Building an App with Real-Time Data Streaming? Consider These Two Platforms.
↗What is a Machine Learning Architect?
↗685: Tools for Building Real-Time Machine Learning Applications — with Richmond Alake
↗Israel's A.I. R&D Edge
↗Leadership Opportunities for Women in STEM
↗684: Get More Language Context out of your LLM — with Jon Krohn (@JonKrohnLearns)
↗How to Monitor Live-Streaming Content and Analyze It for Dangerous Material
↗Using A.I. to Moderate Platform Content
↗683: Contextual A.I. for Adapting to Adversaries — with Dr. Matar Haller
↗XGBoost Prerequisites: What You Should Know Before You Try XGBoost
↗When Not to Use XGBoost
↗When to Use XGBoost
↗682: Business Intelligence Tools — with Mico Yuk
↗XGBoost's Most Important Hyperparameters
↗What is XGBoost
↗681: XGBoost: The Ultimate Classifier — with Matt Harrison
↗How Generative AI will Impact the Labor Market
↗Product-Led Growth: The Key to Rapid SaaS Expansion
↗The Risks for Generative A.I. Founders and Investors
↗680: Automating Industrial Machines with Data Science and the IoT — with Allegra Alessi
↗The Four Layers of the Generative AI Stack
↗How RLHF Makes Apps More Intuitive (Reinforcement Learning from Human Feedback)
↗679: The A.I. and Machine Learning Landscape — with investor George Mathew
↗How A.I. will transform analytics
↗The secret to Google Analytics: Machine Learning
↗Incrementality-centric marketing
↗678: StableLM: Open-source "ChatGPT"-like LLMs you can fit on one GPU — with @JonKrohnLearns
↗Coding skills are an executive's superpower in this data-rich world
↗Brand analytics vs performance analytics: What's the difference?
↗677: Digital Analytics — with Avinash Kaushik
↗What it’s like being a software engineer at Bloomberg
↗Where data analysts should start their learning journey
↗The secrets of matplotlib's ticker module
↗676: The Chinchilla Scaling Laws — with Jon Krohn (@JonKrohnLearns)
↗Should you plot with pandas, matplotlib, or seaborn?
↗3 top tips for pandas data wrangling
↗675: Pandas for Data Analysis and Visualization — with Stefanie Molin
↗To learn from their data, companies should do this
↗How are no-code and low-code tools impacting data science?
↗What do companies that successfully adopt data pipelines have in common?
↗674: Parameter-Efficient Fine-Tuning of LLMs using LoRA (Low-Rank Adaptation) — with Jon Krohn
↗Data pipelines are evolving rapidly. This is how.
↗What are Taipy GUI and Taipy Core? Open-source Python web app-building tools
↗673: Taipy, the open-source Python application builder — with Vincent Gosselin
↗Why you should obtain AWS Certification
↗Essential Machine Learning tools in AWS
↗All data scientists should know these AWS database services
↗672: Open-source "ChatGPT": Alpaca, Vicuña, GPT4All-J, and Dolly 2.0 — with @JonKrohnLearns
↗All data scientists should know these AWS compute and storage services
↗Why cloud skills are essential for data scientists
↗671: Cloud Machine Learning — with Kirill Eremenko and Hadelin de Ponteves
↗Do computer scientists make good product managers?
↗Batch vs stream processing: Which one should you use?
↗2 real-world applications of reactive data processing
↗670: LLaMA: GPT-3 performance, 10x smaller — with Jon Krohn (@JonKrohnLearns)
↗Reactive data processing + IoT = resilient real-world systems
↗What is reactive data processing?
↗669: Streaming, reactive, real-time machine learning — with Adrian Kosowski
↗Post-GPT-4: What commercial A.I. opportunities are coming next?
↗GPT-4: How to accelerate data science with generative A.I.
↗The three GPT4-related attributes companies must have to compete
↗You vs GPT-4: Where can you still add value at work?
↗GPT-4: What tasks does it automate? Which does it augment?
↗667: Harnessing GPT-4 for your Commercial Advantage — with Vin Vashishta
↗A real-life Data Science Superhero: Josh Wills during global catastrophe
↗Management-Track vs Independent Contributor: Which is best for you?
↗The Three Kinds of Data Scientists (and how each can grow their career)
↗666: GPT-4 — with Jon Krohn (@JonKrohnLearns)
↗The "Infinite Loop of Sadness" in data product development (and how to avoid it)
↗Contextual Bandits: better than A/B tests for production ML
↗665: How to be socially impactful and financially successful in your data career — with Josh Wills
↗How to become an A.I. Research Manager at a Big Tech firm
↗A.I. Research in Big Tech vs in academia: the pros and cons
↗Large, multi-year A.I. R&D projects: how to plan them and succeed