Category: deep-learning
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AI in Multiple GPUs: ZeRO & FSDP
AI in Multiple GPUs: ZeRO & FSDP Learn how Zero Redundancy Optimizer works, how to implement it from scratch, and how to use it in PyTorch The post AI in Multiple GPUs: ZeRO & FSDP appeared first on Towards Data Science. Lorenzo Cesconetto Go to original source
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YOLOv3 Paper Walkthrough: Even Better, But Not That Much
YOLOv3 Paper Walkthrough: Even Better, But Not That Much A PyTorch implementation on the YOLOv3 architecture from scratch The post YOLOv3 Paper Walkthrough: Even Better, But Not That Much appeared first on Towards Data Science. Muhammad Ardi Go to original source
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Aliasing in Audio, Easily Explained: From Wagon Wheels to Waveforms
Aliasing in Audio, Easily Explained: From Wagon Wheels to Waveforms Understanding the foundational distortion of digital audio from first principles, with worked examples and visual intuition The post Aliasing in Audio, Easily Explained: From Wagon Wheels to Waveforms appeared first on Towards Data Science. Aman Agrawal Go to original source
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Optimizing Token Generation in PyTorch Decoder Models
Optimizing Token Generation in PyTorch Decoder Models Hiding host-device synchronization via CUDA stream interleaving The post Optimizing Token Generation in PyTorch Decoder Models appeared first on Towards Data Science. Chaim Rand Go to original source
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Optimizing Deep Learning Models with SAM
Optimizing Deep Learning Models with SAM A deep dive into the Sharpness-Aware-Minimization (SAM) algorithm and how it improves the generalizability of modern deep learning models The post Optimizing Deep Learning Models with SAM appeared first on Towards Data Science. Anindya Dey Go to original source
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AlpamayoR1: Large Causal Reasoning Models for Autonomous Driving
AlpamayoR1: Large Causal Reasoning Models for Autonomous Driving All you need to know about Chain of Causation reasoning and the current state of Autonomous Driving! The post AlpamayoR1: Large Causal Reasoning Models for Autonomous Driving appeared first on Towards Data Science. Ryan Pégoud Go to original source
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AI in Multiple GPUs: How GPUs Communicate
AI in Multiple GPUs: How GPUs Communicate A deep dive into the hardware infrastructure that enables multi-GPU communication for AI workloads The post AI in Multiple GPUs: How GPUs Communicate appeared first on Towards Data Science. Lorenzo Cesconetto Go to original source
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Agentic AI for Modern Deep Learning Experimentation
Agentic AI for Modern Deep Learning Experimentation Stop babysitting training runs. Start shipping research. Autonomous experiment management built for/by deep learning engineers. The post Agentic AI for Modern Deep Learning Experimentation appeared first on Towards Data Science. Sam Black Go to original source
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Not All RecSys Problems Are Created Equal
Not All RecSys Problems Are Created Equal How baseline strength, churn, and subjectivity determine complexity The post Not All RecSys Problems Are Created Equal appeared first on Towards Data Science. Diogo Leitão Go to original source
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YOLOv2 & YOLO9000 Paper Walkthrough: Better, Faster, Stronger
YOLOv2 & YOLO9000 Paper Walkthrough: Better, Faster, Stronger From YOLOv1 to YOLOv2: prior box, k-means, Darknet-19, passthrough layer, and more The post YOLOv2 & YOLO9000 Paper Walkthrough: Better, Faster, Stronger appeared first on Towards Data Science. Muhammad Ardi Go to original source
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The Proximity of the Inception Score as an Evaluation Criterion
The Proximity of the Inception Score as an Evaluation Criterion The neighborhood of synthetic data The post The Proximity of the Inception Score as an Evaluation Criterion appeared first on Towards Data Science. Giuseppe Pio Cannata Go to original source
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Optimizing Data Transfer in Distributed AI/ML Training Workloads
Optimizing Data Transfer in Distributed AI/ML Training Workloads A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems – part 3 The post Optimizing Data Transfer in Distributed AI/ML Training Workloads appeared first on Towards Data Science. Chaim Rand Go to original source
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Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels
Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels Why your final LLM layer is OOMing and how to fix it with a custom Triton kernel. The post Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels appeared first on Towards Data Science. Ryan Pégoud Go to original source
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From RGB to Lab: Addressing Color Artifacts in AI Image Compositing
From RGB to Lab: Addressing Color Artifacts in AI Image Compositing A multi-tier approach to segmentation, color correction, and domain-specific enhancement The post From RGB to Lab: Addressing Color Artifacts in AI Image Compositing appeared first on Towards Data Science. Eric Chung Go to original source
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Glitches in the Attention Matrix
Glitches in the Attention Matrix A history of Transformer artifacts and the latest research on how to fix them The post Glitches in the Attention Matrix appeared first on Towards Data Science. Jonathan Williford Go to original source
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Teaching a Neural Network the Mandelbrot Set
Teaching a Neural Network the Mandelbrot Set And why Fourier features change everything The post Teaching a Neural Network the Mandelbrot Set appeared first on Towards Data Science. Carlos Redondo Go to original source
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How to Improve the Performance of Visual Anomaly Detection Models
How to Improve the Performance of Visual Anomaly Detection Models Apply the best methods from academia to get the most out of practical applications The post How to Improve the Performance of Visual Anomaly Detection Models appeared first on Towards Data Science. Aimira Baitieva Go to original source
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YOLOv1 Loss Function Walkthrough: Regression for All
YOLOv1 Loss Function Walkthrough: Regression for All An explanation of how YOLOv1 measures the correctness of its object detection and classification predictions The post YOLOv1 Loss Function Walkthrough: Regression for All appeared first on Towards Data Science. Muhammad Ardi Go to original source
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Optimizing Data Transfer in AI/ML Workloads
Optimizing Data Transfer in AI/ML Workloads A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems The post Optimizing Data Transfer in AI/ML Workloads appeared first on Towards Data Science. Chaim Rand Go to original source
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Deep Reinforcement Learning: The Actor-Critic Method
Deep Reinforcement Learning: The Actor-Critic Method Robot friends collaborate to learn to fly a drone The post Deep Reinforcement Learning: The Actor-Critic Method appeared first on Towards Data Science. Vedant Jumle Go to original source
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Breaking the Hardware Barrier: Software FP8 for Older GPUs
Breaking the Hardware Barrier: Software FP8 for Older GPUs Deep learning workloads are increasingly memory-bound, with GPU cores sitting idle while waiting for data transfers. FP8 precision solves this on newer hardware, but what about the millions of RTX 30 and 20 series GPUs already deployed? Feather demonstrates that software-based FP8 emulation through bitwise packing…
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The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel
The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel An intuitive, step-by-step look at how Transformers use self-attention to turn static word embeddings into contextual representations, illustrated with simple examples and an Excel-friendly walkthrough. The post The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel appeared first on Towards…
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The Machine Learning “Advent Calendar” Day 23: CNN in Excel
The Machine Learning “Advent Calendar” Day 23: CNN in Excel A step-by-step 1D CNN for text, built in Excel, where every filter, weight, and decision is fully visible. The post The Machine Learning “Advent Calendar” Day 23: CNN in Excel appeared first on Towards Data Science. angela shi Go to original source
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Decentralized Computation: The Hidden Principle Behind Deep Learning
Decentralized Computation: The Hidden Principle Behind Deep Learning Most breakthroughs in deep learning — from simple neural networks to large language models — are built upon a principle that is much older than AI itself: decentralization. Instead of relying on a powerful “central planner” coordinating and commanding the behaviors of other components, modern deep-learning-based AI…
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Optimizing PyTorch Model Inference on AWS Graviton
Optimizing PyTorch Model Inference on AWS Graviton Tips for accelerating AI/ML on CPU — Part 2 The post Optimizing PyTorch Model Inference on AWS Graviton appeared first on Towards Data Science. Chaim Rand Go to original source
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Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot
Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot Build a self-hosted, end-to-end platform that gives each user a personal, agentic chatbot that can autonomously vector-search through files that the user explicitly allows it to access. The post Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot appeared first…
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Optimizing PyTorch Model Inference on CPU
Optimizing PyTorch Model Inference on CPU Flyin’ Like a Lion on Intel Xeon The post Optimizing PyTorch Model Inference on CPU appeared first on Towards Data Science. Chaim Rand Go to original source
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Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained
Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained Understanding AI in 2026 — from machine learning to generative models The post Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained appeared first on Towards Data Science. Sabrine Bendimerad Go to original source
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YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World
YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World A detailed walkthrough of the YOLOv1 architecture and its PyTorch implementation from scratch The post YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World appeared first on Towards Data Science. Muhammad Ardi Go to original source
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On the Challenge of Converting TensorFlow Models to PyTorch
On the Challenge of Converting TensorFlow Models to PyTorch How to upgrade and optimize legacy AI/ML models The post On the Challenge of Converting TensorFlow Models to PyTorch appeared first on Towards Data Science. Chaim Rand Go to original source
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Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem
Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem A new NeurIPS 2025 paper shows how self-supervised learning imbues ViT with better image understanding than supervised learning The post Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem appeared first on Towards Data Science. Jonathan Williford Go to original source
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Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch
Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch PyTorch Model Performance Analysis and Optimization — Part 11 The post Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch appeared first on Towards Data Science. Chaim Rand Go to original source
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The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint
The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint Opening the black box of ML models, step by step, directly in Excel The post The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint appeared first on Towards Data Science. angela shi Go to original source
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Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them.
Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them. Neural and symbolic models compress the world in fundamentally different ways, and Sparse Autoencoders (SAEs) offer a bridge to connect them. The post Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them. appeared first on Towards…
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Your Next ‘Large’ Language Model Might Not Be Large After All
Your Next ‘Large’ Language Model Might Not Be Large After All A 27M-parameter model just outperformed giants like DeepSeek R1, o3-mini, and Claude 3.7 on reasoning tasks The post Your Next ‘Large’ Language Model Might Not Be Large After All appeared first on Towards Data Science. Moulik Gupta Go to original source
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How Relevance Models Foreshadowed Transformers for NLP
How Relevance Models Foreshadowed Transformers for NLP Tracing the history of LLM attention: standing on the shoulders of giants The post How Relevance Models Foreshadowed Transformers for NLP appeared first on Towards Data Science. Sean Moran Go to original source
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PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch
PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch Hands-on PyTorch: Building a 3-layer neural network for multiple regression The post PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch appeared first on Towards Data Science. Gustavo Santos Go to original source
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Understanding Convolutional Neural Networks (CNNs) Through Excel
Understanding Convolutional Neural Networks (CNNs) Through Excel Deep learning is often seen as a black box. We know that it learns from data, but we rarely stop to ask how it truly learns. What if we could open that box and watch each step happen right before our eyes? With Excel, we can do exactly…
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I Measured Neural Network Training Every 5 Steps for 10,000 Iterations
I Measured Neural Network Training Every 5 Steps for 10,000 Iterations Image by Pixabay.com The post I Measured Neural Network Training Every 5 Steps for 10,000 Iterations appeared first on Towards Data Science. Javier Marin Go to original source
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We Didn’t Invent Attention — We Just Rediscovered It
We Didn’t Invent Attention — We Just Rediscovered It How selective amplification emerged across evolution, chemistry, and AI through convergent mathematical solutions The post We Didn’t Invent Attention — We Just Rediscovered It appeared first on Towards Data Science. Javier Marin Go to original source
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AI Papers to Read in 2025
AI Papers to Read in 2025 Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science. The post AI Papers to Read in 2025 appeared first on Towards Data Science. Ygor Serpa Go to original source
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From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers
From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers From ARIMA to N-BEATS: Comparing forecasting approaches that balance accuracy, interpretability, and sustainability The post From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers appeared first on Towards Data Science. Dr. Theophano Mitsa Go…
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MobileNetV3 Paper Walkthrough: The Tiny Giant Getting Even Smarter
MobileNetV3 Paper Walkthrough: The Tiny Giant Getting Even Smarter MobileNetV3 with PyTorch — now featuring SE blocks and hard activation functions The post MobileNetV3 Paper Walkthrough: The Tiny Giant Getting Even Smarter appeared first on Towards Data Science. Muhammad Ardi Go to original source
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The Machine Learning Projects Employers Want to See
The Machine Learning Projects Employers Want to See What machine learning projects will actually get you interviews and jobs The post The Machine Learning Projects Employers Want to See appeared first on Towards Data Science. Egor Howell Go to original source
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RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection
RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection From rigid grids to adaptive attention, this is the evolutionary path that made detection transformers fast, flexible, and formidable. The post RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection appeared first on Towards Data Science. David Redó Nieto Go to original…
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Deep Reinforcement Learning: 0 to 100
Deep Reinforcement Learning: 0 to 100 Using RL to teach robots to fly a drone The post Deep Reinforcement Learning: 0 to 100 appeared first on Towards Data Science. Vedant Jumle Go to original source
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When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation
When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation Exploring the frequency fingerprints of Transformers to guide smarter knowledge distillation The post When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation appeared first on Towards Data Science. Ankit Singh Chauhan Go to original source
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Federated Learning and Custom Aggregation Schemes
Federated Learning and Custom Aggregation Schemes A practical guide to designing and analyzing robust aggregation strategies The post Federated Learning and Custom Aggregation Schemes appeared first on Towards Data Science. Salman Toor Go to original source
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How to Classify Lung Cancer Subtype from DNA Copy Numbers Using PyTorch
How to Classify Lung Cancer Subtype from DNA Copy Numbers Using PyTorch A step-by-step introduction to understanding cancer from the perspective of a data scientist. The post How to Classify Lung Cancer Subtype from DNA Copy Numbers Using PyTorch appeared first on Towards Data Science. Adam Streck Go to original source
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Dreaming in Blocks — MineWorld, the Minecraft World Model
Dreaming in Blocks — MineWorld, the Minecraft World Model Explaining “MineWorld: A real-time and open-source interactive world model on Minecraft” in simple terms. The post Dreaming in Blocks — MineWorld, the Minecraft World Model appeared first on Towards Data Science. Youssef Farag Go to original source
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MobileNetV2 Paper Walkthrough: The Smarter Tiny Giant
MobileNetV2 Paper Walkthrough: The Smarter Tiny Giant Understanding and implementing MobileNetV2 with PyTorch — the next generation of MobileNetV1 The post MobileNetV2 Paper Walkthrough: The Smarter Tiny Giant appeared first on Towards Data Science. Muhammad Ardi Go to original source
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Smarter, Not Harder: How AI’s Self-Doubt Unlocks Peak Performance
Smarter, Not Harder: How AI’s Self-Doubt Unlocks Peak Performance “Deep Think with Confidence,” a smarter way to scale reasoning tasks without wasting a massive amount of computation The post Smarter, Not Harder: How AI’s Self-Doubt Unlocks Peak Performance appeared first on Towards Data Science. Ankit Singh Chauhan Go to original source
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How to Improve the Efficiency of Your PyTorch Training Loop
How to Improve the Efficiency of Your PyTorch Training Loop Learn how to diagnose and resolve bottlenecks in PyTorch using the num_workers, pin_memory, and profiler parameters to maximize training performance. The post How to Improve the Efficiency of Your PyTorch Training Loop appeared first on Towards Data Science. Andrea D’Agostino Go to original source
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Visual Pollen Classification Using CNNs and Vision Transformers
Visual Pollen Classification Using CNNs and Vision Transformers Filling the data gap: A machine learning approach to pollen identification in ecology and biotechnology The post Visual Pollen Classification Using CNNs and Vision Transformers appeared first on Towards Data Science. Karol Struniawski Go to original source
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Preparing Video Data for Deep Learning: Introducing Vid Prepper
Preparing Video Data for Deep Learning: Introducing Vid Prepper A guide to fast video data preprocessing for machine learning The post Preparing Video Data for Deep Learning: Introducing Vid Prepper appeared first on Towards Data Science. Jamie Petherbridge-Conroy Go to original source
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Learning Triton One Kernel At a Time: Vector Addition
Learning Triton One Kernel At a Time: Vector Addition The basics of GPU programming, optimisation, and your first Triton kernel The post Learning Triton One Kernel At a Time: Vector Addition appeared first on Towards Data Science. Ryan Pégoud Go to original source
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PyTorch Explained: From Automatic Differentiation to Training Custom Neural Networks
PyTorch Explained: From Automatic Differentiation to Training Custom Neural Networks Deep learning is shaping our world as we speak. In fact, it has been slowly revolutionizing software since the early 2010s. In 2025, PyTorch is at the forefront of this revolution, emerging as one of the most important libraries to train neural networks. Whether you…
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The SyncNet Research Paper, Clearly Explained
The SyncNet Research Paper, Clearly Explained A Deep Dive into “Out of Time: Automated Lip Sync in the Wild” The post The SyncNet Research Paper, Clearly Explained appeared first on Towards Data Science. Aman Agrawal Go to original source
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An Interactive Guide to 4 Fundamental Computer Vision Tasks Using Transformers
An Interactive Guide to 4 Fundamental Computer Vision Tasks Using Transformers An overview of 4 fundamental computer vision tasks – image classification, image segmentation, image captioning and visual question answering, with transformer models. Compare ViT, DETR, BLIP, and ViLT performance interactively by providing a practical Streamlit app implementation guide. The post An Interactive Guide to…
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Building a Unified Intent Recognition Engine
Building a Unified Intent Recognition Engine How modular design can simplify and scale intent classification in enterprise AI systems The post Building a Unified Intent Recognition Engine appeared first on Towards Data Science. Shruti Tiwari Go to original source
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Learn How to Use Transformers with HuggingFace and SpaCy
Learn How to Use Transformers with HuggingFace and SpaCy Mastering NLP with spaCy: Part 4 The post Learn How to Use Transformers with HuggingFace and SpaCy appeared first on Towards Data Science. Marcello Politi Go to original source
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How to Become a Machine Learning Engineer (Step-by-Step)
How to Become a Machine Learning Engineer (Step-by-Step) Your one-stop guide to becoming a machine learning engineer The post How to Become a Machine Learning Engineer (Step-by-Step) appeared first on Towards Data Science. Egor Howell Go to original source
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MobileNetV1 Paper Walkthrough: The Tiny Giant
MobileNetV1 Paper Walkthrough: The Tiny Giant Understanding and implementing MobileNetV1 from scratch with PyTorch The post MobileNetV1 Paper Walkthrough: The Tiny Giant appeared first on Towards Data Science. Muhammad Ardi Go to original source
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What is Universality in LLMs? How to Find Universal Neurons
What is Universality in LLMs? How to Find Universal Neurons How independently trained transformers form same the neurons The post What is Universality in LLMs? How to Find Universal Neurons appeared first on Towards Data Science. Shuyang Go to original source
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Everything I Studied to Become a Machine Learning Engineer (No CS Background)
Everything I Studied to Become a Machine Learning Engineer (No CS Background) The books, courses, and resources I used in my journey. The post Everything I Studied to Become a Machine Learning Engineer (No CS Background) appeared first on Towards Data Science. Egor Howell Go to original source
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Capturing and Deploying PyTorch Models with torch.export
Capturing and Deploying PyTorch Models with torch.export A demonstration of PyTorch’s exciting new export feature on a HuggingFace model The post Capturing and Deploying PyTorch Models with torch.export appeared first on Towards Data Science. Chaim Rand Go to original source
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Maximizing AI/ML Model Performance with PyTorch Compilation
Maximizing AI/ML Model Performance with PyTorch Compilation Since its inception in PyTorch 2.0 in March 2023, the evolution of torch.compile has been one of the most exciting things to follow. Given that PyTorch’s popularity was due to its “Pythonic” nature, its ease of use, and its line-by-line (a.k.a., eager) execution, the success of a just-in-time (JIT) graph…
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From Genes to Neural Networks: Understanding and Building NEAT (Neuro-Evolution of Augmenting Topologies) from Scratch
From Genes to Neural Networks: Understanding and Building NEAT (Neuro-Evolution of Augmenting Topologies) from Scratch Practical Neuroevolution: Reproducing NEAT’s Innovations and Code Walkthrough The post From Genes to Neural Networks: Understanding and Building NEAT (Neuro-Evolution of Augmenting Topologies) from Scratch appeared first on Towards Data Science. Carlos Redondo Go to original source
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The Channel-Wise Attention | Squeeze and Excitation
The Channel-Wise Attention | Squeeze and Excitation Applying the Squeeze and Excitation module on ResNeXt using PyTorch The post The Channel-Wise Attention | Squeeze and Excitation appeared first on Towards Data Science. Muhammad Ardi Go to original source
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Mastering NLP with spaCy – Part 2
Mastering NLP with spaCy – Part 2 POS tagging, dependency parser and named entity recognition. The post Mastering NLP with spaCy – Part 2 appeared first on Towards Data Science. Marcello Politi Go to original source
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How to Benchmark LLMs – ARC AGI 3
How to Benchmark LLMs – ARC AGI 3 Learn how to LLMs are benchmarked, and try out the newly released ARC AGI 3 The post How to Benchmark LLMs – ARC AGI 3 appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Physics-Informed Neural Networks for Inverse PDE Problems
Physics-Informed Neural Networks for Inverse PDE Problems Solving the Heat Equation using DeepXDE. The post Physics-Informed Neural Networks for Inverse PDE Problems appeared first on Towards Data Science. Marco Hening Tallarico Go to original source
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Torchvista: Building an Interactive Pytorch Visualization Package for Notebooks
Torchvista: Building an Interactive Pytorch Visualization Package for Notebooks Building a tool to interactively visualize the forward pass of any Pytorch model from within notebooks. The post Torchvista: Building an Interactive Pytorch Visualization Package for Notebooks appeared first on Towards Data Science. Sachin Hosmani Go to original source
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Gain a Better Understanding of Computer Vision: Dynamic SOLO (SOLOv2) with TensorFlow
Gain a Better Understanding of Computer Vision: Dynamic SOLO (SOLOv2) with TensorFlow A practical approach to instance segmentation using SOLOv2 and TensorFlow The post Gain a Better Understanding of Computer Vision: Dynamic SOLO (SOLOv2) with TensorFlow appeared first on Towards Data Science. Pavel Timonin Go to original source
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Scene Understanding in Action: Real-World Validation of Multimodal AI Integration
Scene Understanding in Action: Real-World Validation of Multimodal AI Integration A deep dive into real-world case studies: from indoor space and urban streets to world-famous landmarks The post Scene Understanding in Action: Real-World Validation of Multimodal AI Integration appeared first on Towards Data Science. Eric Chung Go to original source
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The Crucial Role of NUMA Awareness in High-Performance Deep Learning
The Crucial Role of NUMA Awareness in High-Performance Deep Learning PyTorch model performance analysis and optimization — Part 10 The post The Crucial Role of NUMA Awareness in High-Performance Deep Learning appeared first on Towards Data Science. Chaim Rand Go to original source
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How to Fine-Tune Small Language Models to Think with Reinforcement Learning
How to Fine-Tune Small Language Models to Think with Reinforcement Learning A visual tour and from-scratch guide to train GRPO reasoning models in PyTorch The post How to Fine-Tune Small Language Models to Think with Reinforcement Learning appeared first on Towards Data Science. Avishek Biswas Go to original source
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Build Interactive Machine Learning Apps with Gradio
Build Interactive Machine Learning Apps with Gradio Create a fun text-to-speech demo in minutes The post Build Interactive Machine Learning Apps with Gradio appeared first on Towards Data Science. Ehssan Khan Go to original source
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Taking ResNet to the Next Level
Taking ResNet to the Next Level Understanding how ResNeXt improves upon ResNet, with a comprehensive PyTorch implementation guide The post Taking ResNet to the Next Level appeared first on Towards Data Science. Muhammad Ardi Go to original source
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Four AI Minds in Concert: A Deep Dive into Multimodal AI Fusion
Four AI Minds in Concert: A Deep Dive into Multimodal AI Fusion Introduction: From System Architecture to Algorithmic Execution In my previous article, I explored the architectural foundations of the VisionScout multimodal AI system, tracing its evolution from a simple object detection model into a modular framework. There, I highlighted how careful layering, module boundaries,…
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Pipelining AI/ML Training Workloads with CUDA Streams
Pipelining AI/ML Training Workloads with CUDA Streams PyTorch Model Performance Analysis and Optimization — Part 9 The post Pipelining AI/ML Training Workloads with CUDA Streams appeared first on Towards Data Science. Chaim Rand Go to original source
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Why Your Next LLM Might Not Have A Tokenizer
Why Your Next LLM Might Not Have A Tokenizer The Tokenizer Has Been a Necessary Evil, but This Radical Approach Shows That It Might Not Be Necessary Anymore. The post Why Your Next LLM Might Not Have A Tokenizer appeared first on Towards Data Science. Moulik Gupta Go to original source
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Beyond Model Stacking: The Architecture Principles That Make Multimodal AI Systems Work
Beyond Model Stacking: The Architecture Principles That Make Multimodal AI Systems Work Transforming Independent Models into Collaborative Intelligence The post Beyond Model Stacking: The Architecture Principles That Make Multimodal AI Systems Work appeared first on Towards Data Science. Eric Chung Go to original source
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Can AI Truly Develop a Memory That Adapts Like Ours?
Can AI Truly Develop a Memory That Adapts Like Ours? Exploring Titans: A new architecture equipping LLMs with human-inspired memory that learns and updates itself during test-time. The post Can AI Truly Develop a Memory That Adapts Like Ours? appeared first on Towards Data Science. Moulik Gupta Go to original source
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Audio Spectrogram Transformers Beyond the Lab
Audio Spectrogram Transformers Beyond the Lab A recipe for building a portable soundscape monitoring app with AudioMoth, Raspberry Pi, and a decent dose of deep learning. The post Audio Spectrogram Transformers Beyond the Lab appeared first on Towards Data Science. Maciej Adamiak Go to original source
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A Bird’s-Eye View of Linear Algebra: Measure of a Map — Determinants
A Bird’s-Eye View of Linear Algebra: Measure of a Map — Determinants We roll up our sleeves and start to deal with matrices The post A Bird’s-Eye View of Linear Algebra: Measure of a Map — Determinants appeared first on Towards Data Science. Rohit Pandey Go to original source
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How I Automated My Machine Learning Workflow with Just 10 Lines of Python
How I Automated My Machine Learning Workflow with Just 10 Lines of Python Use LazyPredict and PyCaret to skip the grunt work and jump straight to performance. The post How I Automated My Machine Learning Workflow with Just 10 Lines of Python appeared first on Towards Data Science. Himanshu Sharma Go to original source
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Vision Transformer on a Budget
Vision Transformer on a Budget Introduction The vanilla ViT is problematic. If you take a look at the original ViT paper [1], you’ll notice that although this deep learning model proved to work extremely well, it requires hundreds of millions of labeled training images to achieve this. Well, that’s a lot. This requirement of an enormous…
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Inside Google’s Agent2Agent (A2A) Protocol: Teaching AI Agents to Talk to Each Other
Inside Google’s Agent2Agent (A2A) Protocol: Teaching AI Agents to Talk to Each Other Exploring how Google’s A2A enables plug-and-play communication between LLM-powered agents across frameworks The post Inside Google’s Agent2Agent (A2A) Protocol: Teaching AI Agents to Talk to Each Other appeared first on Towards Data Science. Hailey Quach Go to original source
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Hands-On Attention Mechanism for Time Series Classification, with Python
Hands-On Attention Mechanism for Time Series Classification, with Python This is how to use the attention mechanism in a time series classification framework The post Hands-On Attention Mechanism for Time Series Classification, with Python appeared first on Towards Data Science. Piero Paialunga Go to original source
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A Bird’s Eye View of Linear Algebra: The Basics
A Bird’s Eye View of Linear Algebra: The Basics We think basis-free, we write basis-free, but when the chips are down we close the office door and compute with matrices like fury. The post A Bird’s Eye View of Linear Algebra: The Basics appeared first on Towards Data Science. Rohit Pandey Go to original source
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Detecting Malicious URLs Using LSTM and Google’s BERT Models
Detecting Malicious URLs Using LSTM and Google’s BERT Models A progressive approach to implementing AI-powered webpage detection applications into production The post Detecting Malicious URLs Using LSTM and Google’s BERT Models appeared first on Towards Data Science. Toluwase Babalola Go to original source
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Bayesian Optimization for Hyperparameter Tuning of Deep Learning Models
Bayesian Optimization for Hyperparameter Tuning of Deep Learning Models Explore how Bayesian Optimization outperforms Grid Search in efficiency and performance over binary classification tasks. The post Bayesian Optimization for Hyperparameter Tuning of Deep Learning Models appeared first on Towards Data Science. Kuriko Iwai Go to original source
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Why Regularization Isn’t Enough: A Better Way to Train Neural Networks with Two Objectives
Why Regularization Isn’t Enough: A Better Way to Train Neural Networks with Two Objectives Why splitting your objectives and your model might be the key to better performance and clearer trade-offs in deep learning. The post Why Regularization Isn’t Enough: A Better Way to Train Neural Networks with Two Objectives appeared first on Towards Data…
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The Shadow Side of AutoML: When No-Code Tools Hurt More Than Help
The Shadow Side of AutoML: When No-Code Tools Hurt More Than Help Automl has become the gateway drug to machine learning for many organizations. It promises exactly what teams under pressure want to hear: you bring the data, and we’ll handle the modeling. There are no pipelines to manage, no hyperparameters to tune, and no…