#Deep Machine Learning

I’ve been asked many times where to start learning ML, so after talking to so many experts in this field, this is a good place to start. Comment down below “TRAIN” and I’ll send you a more in-depth checklist along with the best GitHub links to help you start learning each concept. If you don’t receive the link you either need to follow first then comment, or your instagram is outdated. Either way, no worries. send me a dm and I’ll get it to you ASAP. #cs #ai #dev #university #softwareengineer #viral #advice #machinelearning

AI vs Machine Learning VS Deep Learning BREAKDOWN 😤 #ai #ml #tech #fyp

Here’s your full roadmap on how to get into machine learning. Comment “Roadmap” to get the pdf. Save and follow for more. #ai #machinelearning #coding #programming #cs

Machine learning relies heavily on mathematical foundations. #tech #ml #explore #fyp #ai

Machine learning vs Deep learning 🦾 here I have explained what is the difference between machine learning and Deep learning in simple words. To put here again, in a simple manner, there are three main difference First size of the data. Second accuracy level And third, the way they behave behind the scenes. I break down AI so that you can get it for your life, follow @vattsal.ai for more.

These are some of the best beginner-friendly resources I’ve found to actually understand machine learning. Nothing overly complicated, just what you need to get the concepts and start building. Comment ML and I’ll send you all the resources.

Gradient descent is a fundamental optimization algorithm used by most AI models to learn from data by minimizing a loss function, which measures how far the model’s predictions are from the true values. Conceptually, it treats the loss function as a landscape (we call this the loss landscape) with peaks and valleys representing high and low errors. At any point on this landscape, the gradient (vector of slopes) indicates the direction and steepness of the fastest increase in loss. Gradient descent uses the gradient to move in the opposite direction, downhill toward a valley, where the loss is minimized. With each step, the model adjusts its internal parameters (also known as the weights and biases) slightly to reduce the error, slowly improving its performance. This iterative process continues until the model reaches a point where further iterations don’t net much gain in performance. Or, in other words, the loss doesn’t change much. Essentially, this is how nearly all AI models “learn”: by following the gradient of the loss function to find parameter values that produce accurate predictions. C: Welch Labs #machinelearning #deeplearning #statistics #computerscience #coding #mathematics #math #physics #science #education #animation

Let’s see if I can cover the ML pipeline in 60 seconds ⏰😅 Machine learning isn’t just training a model. A production ML lifecycle typically looks like this: 1️⃣ Define the problem & objective 2️⃣ Collect and (if needed) label data 3️⃣ Split into train / validation / test sets 4️⃣ Data preprocessing & feature engineering 5️⃣ Train the model (forward pass + backpropagation in deep learning) 6️⃣ Evaluate on held-out data to measure generalization 7️⃣ Hyperparameter tuning (learning rate, architecture, etc.) 8️⃣ Final testing before release 9️⃣ Deploy (batch inference or real-time serving behind an API) 🔟 Monitor for data drift, concept drift, latency, cost, and reliability 1️⃣1️⃣ Retrain when performance degrades Training updates weights. Evaluation measures performance. Deployment serves predictions. Monitoring keeps the system healthy. It’s not linear. It’s a loop. And once you move beyond a single experiment, that loop becomes a systems problem. At scale, the challenge isn’t just modeling … it’s building reliable, scalable infrastructure that supports the entire lifecycle. Curious if this type of content is helpful! Lmk in the comments & as always Happy Building! 🤍

this is the software side of robotics of course there’s a whole other piece to make the robots work #ai #machinelearning #datascientist #machinelearningengineer #robotics #techcareer #careergrowthtips

Want to become an AI Engineer and earn the highest packages? 🚀 This roadmap breaks down exactly what you need to learn, step by step, to master AI from foundations to advanced MLOps. Stop guessing, start building your dream career! 👇 Drop a comment ‘AI’ and I’ll send you the full roadmap to master AI engineering with top resources! #AIEngineer #AIRoadmap2025 #MachineLearning #DeepLearning #CareerGrowth #TechJobs #AICommunity #Python #MLOps #CodingLife

Day 1 of our Machine Learning series 🚀 We started with the basics — what machine learning really is and how it works. This series is for anyone who wants to understand ML without confusion. Next up: AI vs Machine Learning. . . . . #MachineLearning #ArtificialIntelligence #CodeLoopa #LearnAI #TechExplained

especially when I was studying probabilistic machine learning #questions: which topics you find challenging when studying machine learning/deep learning 🍵 🍵 🍵 #computerscience #datascience #girlwhocodes #codinglife #coding #softwareengineer #studygram #data #machinelearning #womenintech #womenwhocode #tech #learningdiary #ai #researchlife #deeplearning