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We love seeing our learners reach new milestones! 🚀 Huge congratulations to Omar Wael for completing the Machine Learning Specialization! We’re thrilled to see such thoughtful reflections on their journey—take a look at this highlight from Omar's recent post below. Read Omar's full post on our forum to hear more about their experience: Reflections on completing the Machine Learning Specialization #DeepLearningAI# #MachineLearning# #LearnerSpotlight# #Education# #AICommunity#

Harvard University just voted to limit the number of A grades given in undergraduate classes to about 20% of the class. I’m not in favor of this. It deeply runs counter to how I believe education should be. We should hold a high bar, but also work mightily to support the success of 100% of learners, rather than a fraction. Harvard’s administration took this step — over the objections of a large fraction of the student body — to counter grade inflation. Grade inflation is real: Many universities have been awarding A and B grades to ever larger fractions of students, and this has caused grade point averages (GPAs) to become less useful as signals of student skill. At the same time, we want students to succeed. The heart of the question is the role of educational institutions. Should our goal be: - To help students succeed? - To judge students? Both of these have value. But my focus when working in education is almost entirely helping students succeed. To me, it is clear that many people want to learn, to be empowered, to build skills that let them do new things! This is what we focus on at DeepLearningAI. This philosophy is also why my online courses (going back to my early online Stanford courses on Coursera) permitted an unlimited number of retries for graded assignments. I believe in letting — and even encouraging — someone to redo something until they succeed. This is as opposed to standing in judgement of the fact they didn’t get it right the first time. Further, I want homework assignments to be designed primarily to help people practice and learn, rather than to judge their skill level. This is why I prefer to create “Practice Problems” and “Practice Labs” — questions that, when you think through them, help you to gain practice and reinforce what you know. As opposed to “Assessment Problems” designed primarily to judge skill. But won’t Harvard’s move make GPAs more meaningful and help prospective employers identify strong candidates? Having hired a large number of people from Harvard and other institutions, I can say confidently that GPA is not an important signal. We have screening and interviewing processes that give far more accurate ways to figure out if someone is truly skilled. I do not need a wider spread in applicant GPA scores to figure out who's really good! To be clear, there is also value in assessment. Even though standardized testing is much hated, high-quality tests like the SAT, ACT, GRE, TOEFL, etc. provide objective measures of ability in a domain. I find that most people want to learn and succeed. There are also people who want rigorous assessment (for example, to apply for school admissions), but this is a lesser need, and is not my focus when building educational products. Harvard is often described as an “elite” educational institution. There are two ways to be elite: One option involves limiting enrollments, and then even among admitted students, cap the number of people that do well at 20%. I would rather pursue a different path: Set a high bar and teach elite, cutting-edge skills, but strive relentlessly to help everyone succeed. This way, eliteness is defined not by excluding people but by helping as many people as possible to be excellent. [Original text: The Batch newsletter]

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⚡ TL;DR: Can you instantly say how many times faster your model could run on your hardware? SOLAR derives the theoretical best-case runtime (Speed-of-Light) automatically and with validation, straight from PyTorch/JAX code. Title: SOLAR: AI-Powered Speed-of-Light Performance Analysis URL: 📌 Key points ・🤖 An LLM translates code into an IR, then a "generate-then-verify" loop checks it by numerically comparing outputs against the original ・🧮 A deterministic backend derives compute and memory traffic in closed form from just the einsum subscripts ・🎚 Multi-fidelity roofline bounds at three levels: unfused, fused, and cache-aware ・✅ 100% operator coverage on KernelBench's 270 problems with zero SOL violations (existing FLOP counters hit 75-84%) ・🚀 Headroom reaches 54.6x at L3, with fusion analysis surfacing another 7.8x of room ・🦾 All three robotics models on Jetson Thor were memory-bound; 500Hz control needs 19.7x current bandwidth ・🔄 Inverse roofline back-solves the minimum hardware specs needed to hit a latency target 🌍 Takeaway: Blending the flexibility of generative AI with the rigor of analytical math, it's a practical foundation for accelerating performance tuning and hardware selection without physical access. #DeepLearning# #Performance#

🧠 Biologically plausible learning without backpropagation fails on deep networks, and the real culprit is gradient rank collapse. Fixing it with geometric interventions lifts accuracy from 1.4% to 46.1%. Title: Overcoming Rank Collapse in Feedback Alignment URL: 📝 Overview Feedback alignment (FA) is a biologically plausible alternative to backpropagation. It avoids symmetric weight transport, using the same forward weights in the backward pass, and instead learns with fixed random feedback weights. This paper identifies why FA fails to scale to deep architectures, gradient rank collapse, and proposes how to overcome it. ❓ Challenges Solved The FA error has considerably lower rank than backpropagation and is confined to a lower-dimensional subspace. This rank collapse severely limits parameter-space exploration and is the root cause of weight alignment failing to progress during training. 💡 Methodology & Proposed Approach It proposes two complementary mechanisms to raise gradient dimensionality. ・The Muon optimizer, based on orthogonalization, sets every singular value to 1. It flattens the spectrum of momentum updates to emphasize previously neglected directions, rewriting the update geometry itself ・Batch normalization normalizes hidden-layer activations to promote activation orthogonality and maintain high-dimensional representations across layers ・Muon equalizes update geometry while batch norm preserves representational dimensionality; together they maintain richer learning directions throughout training, so alignment emerges even in deeper networks 🎯 Use Cases It is relevant to backprop-free learning, brain-inspired neuromorphic computing, and research on optimization trajectory dimensionality in general. 📊 Experimental Results ・It was evaluated on CIFAR-10, CIFAR-100, STL-10, and Tiny ImageNet with AlexNet and ResNet-18 ・On CIFAR-100 with ResNet-18, baseline FA reached just 1.4%, while FA plus batch norm hit 37.1% and FA plus Muon hit 25.3% ・FA plus Muon plus batch norm reached 46.1%, about a 9-point improvement over batch norm alone ・Similar gains were confirmed across all datasets and architectures #DeepLearning# #Neuroscience#