Going Back to Basics: What Sparked the AI Revolution When I started diving into AI, I thought I needed to jump straight into advanced models. But the real breakthrough came when I slowed down and went back to the basics—understanding why deep learning works and how transformers reshaped the field. Deep learning taught machines to extract features layer by layer. Transformers took it further—making sense of relationships across an entire sequence at once. That shift unlocked today’s wave of innovation: LLMs, generative AI, and agentic systems. 👉 Sometimes the best way to move forward is to look back at the foundations. What “basic” concept in your field changed how you saw the bigger picture? Hashtags: #DeepLearning #Transformers #AIJourney #BackToBasics #ArtificialIntelligence Series tag: #MyAIJourney (Post 1 of 10)
How Deep Learning and Transformers Revolutionized AI
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🧠 Day 2 (12 Weeks) – The Intelligence Stack Today's session mapped how AI branches into Machine Learning, Deep Learning, and now Generative AI. At the core lies Machine Learning — where systems learn from data: • Supervised: learns toward a target • Unsupervised: groups data without one • Reinforcement: improves through rewards Every leap in GenAI stands on these foundations — machines that first learned, then learned to learn. #KapilLearnsAI #GenAI #AIinBanking #MachineLearning #DeepLearning 🔍 How would you explain the difference between ML and DL to a newcomer?
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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
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"Dive deep into the world of generative AI with 'Generative Deep Learning' by David Foster! 🧠🎨 Learn to build models that paint, write, compose, and play, from VAEs and GANs to Transformers and diffusion models. A must-read for anyone eager to create with AI. #GenerativeAI #DeepLearning #MachineLearning"
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Have you ever thought about how an attacker might #reverse-engineer an #AI #model? Our guest today is doing just that, going beyond passwords and keys to unpack the very DNA of #deeplearning ! AI Model Tampering Guest: Yashodhan Mandke, Research Scholar MIT-WPU Check out the Full Episode - S04EP02 | Reversing Large Deep Learning Models
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✨ Visualize. Analyze. Optimize. 🚀 One of the most powerful parts of training deep learning models is using TensorBoard 📊. It turns complex training logs into interactive, easy-to-understand graphs that help you: 🔍 Track performance metrics in real-time 📈 Compare multiple model runs effortlessly ⚡ Spot bottlenecks and optimize faster 🎯 Make data-driven decisions during training Visualization isn’t just pretty graphs—it’s how you unlock insights and make your models smarter 💡. 👉 Follow me Joel Nadar for more AI, Computer Vision & Deep Learning insights! #TensorBoard #DeepLearning #MachineLearning #AI #ComputerVision #DataScience #NeuralNetworks #Research #Innovation
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In compliance, we’re often asked to weigh in on AI without everyone sharing the same definition. That’s where misunderstandings start. This 10-minute video from IBM is one of the clearest overviews I’ve seen on the difference between Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI. For anyone new to the topic, or looking for a refresher, it is a good starting point. 👉 https://lnkd.in/eCuFdwSe
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A common mistake we see in #AI learning paths: jumping straight into deep learning, without first building a foundation in machine learning. In this short podcast snippet, we share why the smarter path is: 1️⃣ Start with #MachineLearning: learn data prep, linear models, decision trees, and how to explain why a model predicts what it does. 2️⃣ Then move into #DeepLearning: scale up with neural networks and big data, once you have the fundamentals down. This approach saves teams from wasting time debugging black-box models and helps them ship solutions that actually work. At ClickIT, we guide engineering teams through this journey, building strong #ML foundations before scaling with #DL and #GenAI. 🔗 Learn more about our Machine Learning services: https://lnkd.in/gpHdKymZ #AILearning #MachineLearningvsDeepLearning #MLvsDL
It's intriguing how revisiting core concepts offers clarity! For me, grasping basic logic principles transformed my approach to complex problem-solving. Simplicity really does lead to breakthrough innovations. #BackToBasics