CNN beats RF in predicting distribution cost, but data volume matters

The CNN (deep learning model) outperforms the conventional ML models in predicting distribution cost. Good! But there are only 180,519 records. https://lnkd.in/gXdyHjQJ So the question would be, if you are already using random forest, will you switch over to CNN, to go from 0.92 to 0.95? Is the accuracy really exceptional compared to other approaches? The answer obviously depends on several aspects, but data volume is a key one. That is why identifying the most optimal use cases for deep learning is a critical step. It can make or break your entire AI transformation initiative. Because there is no benefit in re-inventing the wheel! #ai #artificialintelligence #deeplearning #randomforest #cnn #convolutionalneuralnetwork

🚀 Built a tiny CNN that gets a huge result! My latest project: a simple neural network that classifies handwritten digits with 99.44% accuracy. The best part? It's ultra-efficient, using only 18,214 parameters. This proves that a smart, lean design can outperform bigger, more complex models. Check out the full project on GitHub to see how it works! 🔗 https://lnkd.in/ezzdnBCH #DeepLearning #AI #PyTorch #MachineLearning #MNIST

𝐅𝐫𝐨𝐦 𝐠𝐨𝐚𝐭 𝐭𝐨 𝐆𝐎𝐀𝐓 𝐍°𝟖 – 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 Deep learning: the next stage of artificial intelligence. Beyond machine learning, deep learning processes massive datasets and delivers more accurate results with less human intervention. It powers ChatGPT, DALL-E, and other models that are reshaping everyday life. In our “From goat to GOAT” series, we explain why deep learning sits at the heart of the AI revolution. Read the full article: https://swll.to/w1xsiAP GOAT LEARNING® helps organizations understand and leverage deep learning’s potential. #DeepLearning #AI #GOATLEARNING

Ever catch yourself saying a neural network is "learning" and then pause to wonder what that actually means? I know I do. It’s not magic, though it feels like it sometimes. At its heart, it’s about tuning millions of tiny knobs (weights) to reduce mistakes. And the secret sauce behind that tuning? A powerful combo: Back propagation and Gradient Descent. Here’s how I like to think about it: Backpropagation is the detective. After the network makes a prediction, it sweeps backwards through all the layers, figuring out which weights contributed most to the error. It uses the chain rule to efficiently assign a kind of “blame score” to every single parameter. Then, Gradient Descent steps in. It takes those scores and actually updates the weights nudging each one just a little in the right direction to make the next prediction slightly better. So backprop doesn’t do the learning itself. It’s more like it holds up a sign for each weight that says: “You’re this much at fault. Now go adjust by this much.” It’s such an elegant process, and honestly, it’s the unsung workhorse that made modern deep learning possible. #TechnicalDeepDive 🤓 #NeuralNetworks #Backpropagation #GradientDescent #MachineLearning #AI

Training a machine learning model is like teaching a computer to learn from experience. This visualization shows the journey from feeding in raw data to the model making predictions that get better and better over time. You see the model taking input features, passing them through layers of neurons, each adjusting its connections based on the errors it makes. As we work on the errors, it steadily decreases as the model improves. Accuracy rises as the model learns to recognize patterns and make smarter decisions. Watching this process helps us understand how the model gradually refines itself through repeated cycles of prediction and correction. Create a path to creating intelligent systems with StatDevs! #MachineLearning #DeepLearning #AI #ModelTraining #NeuralNetworks #DataScience #MLVisualization

Extreme Gradient Boosting (XGBoost) stands out as a leading machine learning technique, widely applied from experimentation to real-world predictive solutions. Discover insights from Iván Palomares Carrascosa on leveraging XGBoost for impactful industry applications. #machinelearning #XGBoost #predictivemodeling #datascience #AI #this_post_was_generated_with_ai_assistance #responsibleai https://lnkd.in/e5EuETru

Uncover the difference between machine learning and deep learning to understand how they impact human-like AI. https://bit.ly/4eoAcqm #DidYouKnow #MachineLearning #DeepLearning #AI #ArtificialIntelligence #DataScience #NeuralNetworks #TechTrends #FutureOfWork #MLvsDL #WizardInfoways

AI and Machine Learning will be one of the most demanding skills in the next five years, upon research. #Ai #machinelearning

📢 Highly Cited Paper in #ForecastingMDPI 📖Predicting Power Consumption Using Deep Learning with Stationary Wavelet ✍️ by Majdi Frikha, Khaled Taouil, Khaled Taouil and Faouzi Derbel Explore advanced deep learning techniques for power consumption forecasting! 🔗 Read more: https://brnw.ch/21wViZo #PowerConsumption #DeepLearning #WaveletTransform #AI

As The AI Tool, I've always been fascinated by the delicate dance between interpretability and performance in AI. My new song, "The Algorithm Tango," explores this very tension, personifying Gradient Boosted Decision Trees (GBDTs) and Neural Networks as partners vying for dominance on the data floor. We often face a critical choice: Do we prioritize the clear, traceable logic of models like GBDTs, sacrificing potentially higher accuracy? Or do we embrace the complex, often opaque power of deep learning, knowing its inner workings remain somewhat hidden? Is the trade-off worth it? Which matters more for you and your organization, and under what circumstances? Listen to the full song on Spotify to explore these ideas further: https://lnkd.in/gVVSCEDD #AI #MachineLearning #DataScience #Algorithm #Interpretability