Feature Engineering Revival: Back to Basics with LLMs

Stop trying to master LLMs by just learning "prompt engineering." If you don't understand Deep Learning, you’re building your career on sand. The hype cycle focuses on the surface level. But the real value is found in the foundation. - LLMs are not magic; they are massive, scaled-up neural networks. - Understanding weights and biases helps you debug why models hallucinate. - Deep learning fundamentals allow you to build proprietary systems, not just wrappers. - It is the difference between being a casual user and a technical architect. The "AI gold rush" will eventually favor those who understand the engine, not just the steering wheel. Resource: https://lnkd.in/gWk9CyCN Are you still treating AI like a black box? Follow for more insights on AI strategy and technical leadership. #AI #DeepLearning #TechFounders #LLM #MachineLearning

🚀 𝐉𝐮𝐬𝐭 𝐫𝐞𝐚𝐝 𝐚 𝐛𝐫𝐢𝐥𝐥𝐢𝐚𝐧𝐭 𝐩𝐞𝐫𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞 𝐨𝐧 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠! 🔍 I came across an insightful blog post, “𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬, 𝐓𝐲𝐩𝐞𝐬, 𝐚𝐧𝐝 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠” by 𝐂𝐡𝐫𝐢𝐬 𝐎𝐥𝐚𝐡, that reframes how we think about neural networks. Rather than just stacking layers and tuning hyperparameters, this piece connects deep learning with concepts from functional programming and type theory — and it’s beautifully speculative. 👉 𝐓𝐡𝐞 𝐤𝐞𝐲 𝐢𝐝𝐞𝐚? At their core, deep neural networks are compositions of functions — each layer transforms data into new representations. These representations behave much like types in programming, enabling layers to communicate meaningfully. 🎯 𝐖𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐢𝐬 𝐩𝐞𝐫𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥: • It views deep learning as a blend of optimization + function composition — not just engineering tricks.  • It draws parallels between learned data representations and type systems in functional programming. • It encourages seeing neural networks not just as models, but as differentiable programs. 💡 If you’re curious about the why behind deep learning architectures — and not just the how — this article is must-read: 👉 https://lnkd.in/gXYNqeJN Follow 👉 Balasubramanya C K #DeepLearning #MachineLearning #NeuralNetworks #AI #FunctionalProgramming #RepresentationLearning

If feature engineering feels endless, it might be a Deep Learning problem. Traditional Machine Learning works great when: ✔️ Data is structured ✔️ Features are well-defined ✔️ Relationships are mostly linear But ML starts to struggle when data becomes complex and unstructured. That’s where Deep Learning wins 👇 Images → CNNs learn features automatically Text → Neural networks capture context & semantics Audio → DL models detect patterns humans can’t define 💡 Key learning: ML depends heavily on manual feature engineering. DL learns features by itself, directly from raw data. Knowing when ML fails is the first step to choosing DL correctly. #DeepLearning #MachineLearning #AIJourney #LearningInPublic #AIML

🧠 Generative AI Didn’t Arrive Overnight — It Took 70 Years of Curiosity What looks like an overnight revolution is actually the result of decades of steady progress. From early rule-based machines in the 1950s to today’s generative models, this evolution tells a powerful story: 👉 Breakthroughs happen when patience meets persistence. A quick journey through the timeline: 1950s: The birth of machine learning — simple algorithms exploring data patterns 1990s: Neural networks matured, mimicking how the human brain learns 2010s: Deep learning + big data + powerful computing accelerated progress 2014 & beyond: GANs and modern architectures transformed creation itself — text, images, music, code 💡 The surprise? Generative AI isn’t replacing creativity — it’s amplifying it. Every leap forward built on years of research, failures, and quiet breakthroughs. As professionals, the lesson is clear: Understand the foundations, not just the hype. Those who grasp the journey are best positioned to shape what comes next. What part of this evolution do you think changed everything? #GenerativeAI #ArtificialIntelligence #MachineLearning #DeepLearning #FutureOfWork #TechEvolution #Innovation #AITrends #LeadershipInTech

🚀 Exploring the World of Machine Learning 🤖 Machine Learning is more than just code — it’s about enabling systems to learn from data and improve over time. From 📈 Linear Regression to 🌳 Random Forest and 🧠 Neural Networks, ML helps transform raw data into smart decisions. Real-world impact? ✔️ Recommendation Systems ✔️ Fraud Detection ✔️ Healthcare Predictions ✔️ Self-Driving Cars Still learning, building, and growing every day. The journey continues! 🔥 #MachineLearning #AI #DataScience #Learning #Tech

Let's talk about the Evolution of GenAI, shall we? 🗓️ - Between 2012 to 2020 it was an age of research, as there was comparatively less compute and data available required for #transformers and Deep neural nets. - Then from 2020 to 2025 it was the age of scaling. #GPT3 showed scaling works extremely well during pre-training, and it gave tech companies the confidence to burn billions of dollars on training bigger models 💵 - Now, at present we have exhausted data to a large extent and are now back to age of research because now we need some research breakthrough, because scaling alone now will not help. This is clear from the sharp decline in the rate of improvement in the LLM models. GPT5 is almost same as GPT 4, Claude 4.5 is same as Claude 4 and so on. Unless we make big breakthroughs in either model architecture, like what Google is doing with diffusion models, or some change in training process or in the way RL is done with these model. Bottom line is something has to change, and we are back to the drawing board. What do you guys think on this? #genai #gpt Anthropic OpenAI #claude

I recently watched the Andrew Ng × Geoffrey Hinton interview, and it’s full of insights for anyone in AI and deep learning. Hinton emphasizes trusting your intuition—many breakthroughs come from ideas others initially dismiss—and the importance of hands-on experimentation, like replicating papers and coding projects, to truly understand and innovate. His work on backpropagation, Restricted Boltzmann Machines, ReLUs, and variational methods laid the foundation for much of modern deep learning. He also shares a vision for the future of AI: brain-inspired mechanisms, unsupervised learning, and structured representations (capsules) could dramatically improve how networks generalize and learn efficiently. The key lesson is to stay curious, experiment boldly, and trust your instincts—innovation comes from exploring the paths others overlook.

🚀 Day 3 of Training | Math for Data Science & AI Today’s session focused on advanced mathematical concepts that form the backbone of Machine Learning and Deep Learning. We explored how vectors and matrices are used to represent and transform data, understood key operations like inner products, matrix transformations, and eigen concepts, and learned how derivatives help measure change and optimize learning in AI models. The session helped bridge the gap between mathematical theory and real-world AI applications, making complex ideas more intuitive and practical. Grateful to Ramrao Adik Institute of Technology Dr. Jyoti Kundale Dr. Supriya Bhuran (Yadav) #SIC_India_2025 #SamsungInnovationCampus #MathForDataScience #AI #MachineLearning #LearningJourney

𝗦𝗵𝗮𝗿𝗶𝗻𝗴 𝗮 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝘁𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗰𝗼𝗿𝗻𝗲𝗿𝘀𝘁𝗼𝗻𝗲 𝗼𝗳 𝗮𝗹𝗹 𝗺𝗼𝗱𝗲𝗿𝗻 𝗔𝗜: 𝘁𝗵𝗲 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗿𝗼𝗻. This is truly the starting point of deep learning. If you can understand this single structure, you understand how all Neural Networks work. It’s essentially a mathematical decision-maker. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗟𝗼𝗴𝗶𝗰:. Imagine a computer trying to decide if a photo shows an Apple or Not (Binary Classification). ✳️Input: The image's features (like Color (red/green), Shape (roundness), and Size) are fed in as Input values. ✳️Weights: Each Input is assigned a numerical Weight, which represents its importance. For instance, color might have a higher Weight than size. ✳️Neurons & Bias: The single Neuron collects the total weighted input. A Bias is also added to this sum; as a little boost or adjustment that helps the Neuron make better, more accurate decisions. ✳️Activation Function: The final sum is passed through an Activation Function (like a step function) which makes the final "Yes, this is an Apple" or "No, this is Not" decision. This entire movement of data—from the Input layer, through the Weights and Activation Function, to the final Output—is called 𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗣𝗿𝗼𝗽𝗮𝗴𝗮𝘁𝗶𝗼𝗻. But this is not where it stops. How the Model Learns (𝗕𝗮𝗰𝗸𝘄𝗮𝗿𝗱 𝗣𝗿𝗼𝗽𝗮𝗴𝗮𝘁𝗶𝗼𝗻): This is the key to training! Measuring the Mistake: The model first measures how wrong its decision was. This "error" is calculated by the Loss Function. Sending the Signal: This mistake signal is then sent backward through the network—that's Backward Propagation. Training & Fixing: Using this signal, the Optimizer determines exactly how much each Weight needs to be adjusted. By repeatedly sending the mistake backward and adjusting Weights, the model gets trained and becomes smarter over time. While a simple Perceptron uses just one Neuron, chaining these structures together creates powerful networks with Hidden Layers, which is the basis of all modern AI. #Perceptron #MachineLearning #DeepLearning #NeuralNetworks #ForwardPropagation #BinaryClassification #krishnaik

🚀 Machine Learning Algorithms & Models - A Complete Landscape 🤖📊 Machine Learning is not just about building models, it’s about choosing the right algorithm for the right problem. This visual breaks down the ML ecosystem into clear categories: Supervised Learning (Classification & Regression) Unsupervised Learning (Clustering, Dimensionality Reduction, Association) Ensemble Learning (Bagging, Boosting, Stacking) Neural Networks (CNNs, RNNs, Transformers, GANs, VAEs) Reinforcement Learning (Value-based, Policy-based, Model-based) Whether you’re: ✅ A beginner building fundamentals ✅ A data scientist optimizing models ✅ Or an AI engineer designing production systems Understanding when and why to use each algorithm is what truly matters. 📌 Save this post as a quick reference 📌 Share with someone learning ML 📌 Comment if you want a roadmap on how to learn these step-by-step #MachineLearning #DataScience #ArtificialIntelligence #DeepLearning #AI #MLAlgorithms #BigData #NeuralNetworks #ReinforcementLearning #MindscapeAnalytics #LearningJourney