David Rossitter’s Post

When building an artificial intelligence model I have a habit of first building what I term a "Benchmark" model. It's always a shallow network of one or two layers with no regularizations. Oftentimes time some of my closest friends also in the field asked why I always take my time to build a "Shallow" model and my response has always been that it helps me understand exactly what I'm working against. Building an AI model is just a means to an end and that end is the most important thing therefore, the intention of building an AI model isn't to tell or more about what it has been exposed to during training and that's because it already has sufficient information and could easily map a prediction but what is more important is how a model performs when it's exposed to entirely new information. Building a model that generalizes well on this new dataset involves several optimizations and this process becomes easier when you begin to optimize from the least model accuracy or performance. Even though my experiences can dictate some assumptions such as the number of layers, networks, learning rate, optimizer, etc that can give optimal performance however I always unconsciously build a "Shallow Model" for any project I am about to undertake. It helps. #ArtificialIntelligence #Machinelearning

Zero-Shot Inference: Making Predictions Without Seeing Examples Zero-shot inference is a technique where a model can classify or predict information for a completely new category, even if it hasn’t seen any training examples for that specific category. This is achieved by leveraging additional information and the model’s ability to understand relationships between concepts. I wrote a post on this topic please read this using this link : https://lnkd.in/dMXp7pDE #ZeroShotInference #MachineLearning #AI #DataScience #Innovation #Technology #DeepLearning #NeuralNetworks #PredictiveModeling #ZeroShotLearning #ZeroShotPrediction #NewFrontiers #FutureTech #NextGenAI #InferenceModels #ConceptualUnderstanding #EmergingTech #LinkedinPost #KnowledgeSharing #ArtificialIntelligence #MLResearch #ZeroShotLearningExplained #ZeroShotInferenceExplained #CuttingEdgeTech #StayUpdated

Machine learning algorithms can sift through massive amounts of data to uncover hidden patterns and make accurate predictions, while forecasting techniques help to predict future outcomes based on past trends. Read more 👉 https://lttr.ai/AQCLZ #DigitalMarketing #RequiresDomainKnowledge #PotentialCriminalActivity #EfficientPolicingEfforts #TraditionalTrackingMethods #TodaySDataRichEnvironment #TodaySDataDrivenWorld #SocialTrafficAttribution #ProvideValuableInformation #IncorporateMultipleLayers

𝐉𝐮𝐬𝐭 𝐥𝐢𝐤𝐞 𝐰𝐚𝐭𝐞𝐫 𝐜𝐫𝐚𝐬𝐡𝐢𝐧𝐠 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐫𝐨𝐜𝐤𝐬 𝐬𝐡𝐚𝐩𝐞𝐬 𝐭𝐡𝐞𝐦 𝐨𝐯𝐞𝐫 𝐭𝐢𝐦𝐞, 𝐝𝐚𝐭𝐚 𝐫𝐞𝐬𝐡𝐚𝐩𝐞𝐬 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬. The power isn’t in the single wave, but in the persistence. How are you shaping your AI models with data? If you're not consistently refining, you're missing opportunities for growth. 𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐭𝐨 𝐡𝐚𝐫𝐧𝐞𝐬𝐬 𝐝𝐚𝐭𝐚’𝐬 𝐭𝐫𝐮𝐞 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐢𝐧 𝐌𝐋: 𝐒𝐭𝐚𝐫𝐭 𝐬𝐦𝐚𝐥𝐥, 𝐢𝐭𝐞𝐫𝐚𝐭𝐞 𝐟𝐚𝐬𝐭:   ↳ Test hypotheses quickly    ↳ Learn from each iteration    ↳ Adapt based on feedback 𝐔𝐬𝐞 𝐝𝐢𝐯𝐞𝐫𝐬𝐞 𝐝𝐚𝐭𝐚 𝐬𝐨𝐮𝐫𝐜𝐞𝐬:   ↳ Broaden your dataset variety    ↳ Capture multiple perspectives    ↳ Boost model generalization 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐭𝐡𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐜𝐲𝐜𝐥𝐞:    ↳ Reduce manual intervention    ↳ Scale efficiently    ↳ Ensure continuous improvement 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐦𝐨𝐝𝐞𝐥 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞:    ↳ Track relevant metrics    ↳ Monitor over time    ↳ Learn from changes 𝐀𝐝𝐝𝐫𝐞𝐬𝐬 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐞𝐚𝐫𝐥𝐲:    ↳ Clean and preprocess    ↳ Handle missing data    ↳ Reduce bias upfront Ready to see the power of persistence in action? Start with a small project. Experiment, iterate, and let the data do the work. The results will speak for themselves. What’s your first experiment going to be? ♻️ Repost to your LinkedIn followers and follow Timothy Goebel for more actionable insights on AI and innovation. #MachineLearning #DataDrivenAI #AIInnovation #ContinuousImprovement #AIExperimentation

🚀 Mastering Hyperparameter Tuning and Overfitting in Machine Learning 🧠 Understanding the balance between bias and variance is crucial when selecting hyperparameters. It’s not just about minimizing loss, but about finding the right balance to avoid overfitting and underfitting. 🔑 Key Concepts: Overfitting: When the model excels on training data but underperforms on validation data. Underfitting: When the model struggles on both training and validation sets. Training Loss: Error calculated on the training data. Validation Loss: Error calculated on the validation data to help diagnose performance issues. 🛠️ Machine Learning Workflow: Data Preparation: Split your data into training (80-90%), validation, and test sets. Training Phase: Train the model and monitor training loss. Validation Phase: Use validation loss to assess if the model is overfitting or underfitting. Stopping Criteria: Stop training when validation error starts increasing, signaling overfitting. ✅ Pro Tips: If validation loss is much higher than training loss, your model might be overfitting. Always maintain a test set for final evaluation to avoid bias in model assessment. By carefully managing these aspects, you can develop robust models that generalize well to new data. #MachineLearning #DataScience #AI #Overfitting #HyperparameterTuning #ModelTraining #MLWorkflow

In today’s rapidly evolving world, Machine Learning isn’t just about algorithms or data — it’s about shaping the future through intelligence and innovation. Every line of code we write, every model we train, transforms raw data into meaningful insights that drive real-world solutions. 🌍 Machine Learning is more than just technology; it’s a powerful tool that helps industries make smarter decisions, enhance efficiency, and create personalized experiences for billions of people. From healthcare to finance, from retail to entertainment, Machine Learning is redefining what’s possible. 💡 As we continue to explore the endless possibilities of AI, let’s inspire one another to keep learning, experimenting, and building solutions that matter. Whether you’re just beginning your journey or are deep into the field, know that your contribution has the power to impact the world in ways you may never have imagined. Together, let’s push the boundaries of technology and lead the next wave of innovation! The future belongs to those who dare to dream big and work towards it every day. 🔍 What’s your next Machine Learning goal? Let’s connect, share, and grow together! 💪 #MachineLearning #ArtificialIntelligence #Innovation #AIRevolution #DataScience #TechCommunity #FutureTech #Inspiration #GrowthMindset

It's not a secret...everybody's sprinting to integrate AI into their products 🏃♂️💨 But, you've GOT to nail down the fundamentals to get it right. I've worked directly in AI for over 4 years. Lots has changed, although, the foundations have stayed the same. For the builders out there - here are 3 principles to keep in mind when starting your AI journey: 1. Data is everything 📊 Garbage in = garbage out. Your models are only as good as the data they're trained on. Data Engineering might be the 'unsexy' side of things, but it's essential in order to get the downstream components right. 2. Get clear on your "why" 💭 Building tech in search of a problem seldom works. Lots of companies want to throw machine learning in the mix without getting clear on the value add. Instead, make sure to have a defined roadmap of how AI solves your customer/organization's needs. 3. Education is key 📚 This space moves faaaast. Building on the point above - really take time to understand where the technology is at, and have a process in place to stay on top of trends. This'll help you get clear on the capabilities, limitations and opportunities. -- What would you add to this list? Share your thoughts in the comments below 👇 #artificialintelligence #machinelearning #technology

𝐃𝐚𝐲 𝟑/𝟑𝟎: 𝐃𝐞𝐥𝐯𝐢𝐧𝐠 𝐢𝐧𝐭𝐨 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧! 🧠📊 Hello, LinkedIn community! 👋 Today, we’re diving into one of the most fundamental tasks in machine learning: 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧. Whether it's spam detection, image recognition, or medical diagnosis, classification models are everywhere! We’ve put together a PDF summarizing the key concepts of 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧, including: - 𝐁𝐢𝐧𝐚𝐫𝐲 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Distinguishing between two classes. - 𝐌𝐮𝐥𝐭𝐢𝐜𝐥𝐚𝐬𝐬 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Handling more than two classes. - 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐌𝐞𝐭𝐫𝐢𝐜𝐬: Understanding accuracy, precision, recall, F1 score, and more. - 𝐂𝐨𝐧𝐟𝐮𝐬𝐢𝐨𝐧 𝐌𝐚𝐭𝐫𝐢𝐱: A detailed breakdown of model performance. - 𝐄𝐫𝐫𝐨𝐫 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Identifying and understanding model errors to improve performance. 𝐋𝐞𝐭’𝐬 𝐦𝐚𝐤𝐞 𝐢𝐭 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞! What classification problem are you most interested in solving? Is it text classification, image recognition, or something else? Share your thoughts in the comments below! Stay tuned for tomorrow’s post, where we’ll explore the exciting topic of 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬! Let’s continue this learning adventure together! 🚀 #Day3 #DataScience #MachineLearning #AI #Classification #LearningJourney #ArtificialIntelligence #DeepLearning #DataDriven #ML #ModelTraining

Machine Learning #ML is transforming industries, but what fuels its success? Dive into the world of Knowledge Graphs and their role in driving ML in our latest article! #NebulaGraph #NebulaGraphDB

Completed: Survival Analytics in My Machine Learning Journey! I'm excited to share that I’ve successfully completed learning Survival Analytics! This powerful technique helps model time-to-event data, which is crucial in fields like healthcare, customer retention, and reliability engineering. 🔍 What I Learned: Kaplan-Meier Estimators to estimate survival functions over time.. Managing censoring in survival data, where events haven't occurred yet for some observations. Real-world applications like predicting patient survival, customer churn, and product failure times. #SurvivalAnalytics #MachineLearning #DataScience #AI #TimeToEvent #LearningJourney #PredictiveAnalytics