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  • Deep Learning
  • Algo(30/40)The Great CNN “Zoo” & Its Tools: VGG, Inception & Batch Norm (2014–2015)
  • DeepSeek AI: Smarter AI for Complex Challenges | Simplilearn

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ministryoftesting.com /3 weeks ago

Deep Learning

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medium.com /1 month ago

Algo(30/40)The Great CNN “Zoo” & Its Tools: VGG, Inception & Batch Norm (2014–2015)

In 2012, AlexNet proved that Deep Learning worked. But it was a messy, hand-tuned architecture that felt more like alchemy than…Continue reading on Medium »

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simplilearn.com /1 month ago

DeepSeek AI: Smarter AI for Complex Challenges | Simplilearn

Artificial intelligence is moving ahead quickly, and DeepSeek AI is stealing the spotlight. Developed by High Flyer Capital Management, DeepSeek is already outperforming some of th...

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mql5.com /1 month ago

The Neural Revolution: How Deep Learning Transforms the Architecture of Quantitative Trading

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pandaily.com /2 weeks ago

Peking University and DeepSeek Open-Source DSpark, Delivering Major Leap in LLM Inference Efficiency

Peking University and DeepSeek jointly open-source DSpark, a speculative decoding framework that boosts LLM inference speed by 60-85% with up to 661% throughput gain under strict l...

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dataquest.io /1 month ago

Best Deep Learning Courses in 2026

Most "best deep learning courses" lists mix up three very different things: general AI literacy courses, courses about using pre-trained models, and courses that actually teach you...

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developer-tech.com /3 weeks ago

NVIDIA: DFlash block diffusion accelerates autoregressive LLMs

Deploying DFlash block diffusion on NVIDIA hardware accelerates autoregressive LLMs during latency-sensitive inference. Autoregressive large language models generate tokens sequent...

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pandaily.com /1 month ago

Stepfun Open-Sources Step 3.7 Flash LLM Optimized for Agent Era

Stepfun open-sources Step 3.7 Flash, a 196B-parameter sparse MoE LLM optimized for agent workflows with 400 tokens/s speed and native tool-calling capabilities.

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pandaily.com /2 weeks ago

Inference Takes Center Stage: DeepSeek DSpark Accelerates Generation by 80%

DeepSeek's DSpark speculative decoding framework marks a strategic shift as AI competition moves from training scale to inference efficiency and real-world deployment.

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dev.to /2 weeks ago

Ollama's Chinese Model Support Is Real — But Running Kimi and DeepSeek Locally Has a Hidden Cost

Your error rate just spiked 12%. Three weeks of debugging, $40k in developer hours, and the coffee's cold. The terminal is still red. You've been burning through API credits callin...

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medium.com /1 month ago

Notes from my ongoing AI learning journey

I have been trying to find my way into machine learning and AI for a long time. I am a data engineer, so I am not new to data, systems, or…Continue reading on Medium »

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medium.com /1 month ago

Machine Learning: How Computers Learn from Data

Machine Learning: How Computers Learn from DataContinue reading on Medium »

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pandaily.com /2 weeks ago

DeepSeek DSpark Boosts Generation Speed by 85% in First Post-Funding Paper

DeepSeek releases DSpark speculative decoding framework, boosting generation speed by 85% in Liang Wenfeng's first paper after the company's $7B funding round.

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medium.com /4 weeks ago

Perceptron and Multi-Layer Perceptron: Understanding the Building Blocks of Neural Networks

IntroductionContinue reading on Medium »

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dev.to /1 week ago

Choosing Between DeepSeek, Qwen, Kimi, and GLM at Scale

Choosing Between DeepSeek, Qwen, Kimi, and GLM at Scale Six months ago, my cloud bill was scaring me. We were running everything through a single Western LLM provider, and the cos...

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medium.com /1 month ago

Algo(31/40)Real-World Perception & Action: Pixels, Boxes & Trust (2015)

By 2015, Neural Networks were excellent at saying “This is a cat.” But in the real world, that isn’t enough. A self-driving car needs to…Continue reading on Medium »

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medium.com /1 month ago

TinyML : Deploying ML model to a microcontroller

Let’s be honest.Continue reading on Medium »

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medium.com /5 days ago

Understanding Transformers (Part 5): The final layers doing some heavy lifting

LayerNorm, residuals, feed-forward blocks, and the encoder-decoder pipeContinue reading on Data Science Collective »

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venturebeat.com /2 weeks ago

DeepSeek open sources DSpark, a new framework to speed up LLM inference by up to 85%

Even as the geopolitical conversation around AI continues to grow more fraught following the U.S. government's actions to limit the new models from Anthropic and OpenAI, Chinese op...

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bioengineer.org /1 month ago

Hybrid Deep Learning Enhances Pressure Analysis in Reservoirs

In the rapidly evolving domain of subsurface reservoir engineering, a groundbreaking study has emerged, promising to revolutionize how pressure transients are analyzed in complex g...

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medium.com /1 month ago

Edge AI for Android Developers: Bringing Intelligence Closer to the User

Over the last few years, Artificial Intelligence has become a core part of mobile applications. Features such as face unlock, voice…Continue reading on Medium »

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pandaily.com /1 week ago

DeepSeek and Zhipu AI Begin Self-Developing AI Chips — The Endgame Is No Longer Just Models

DeepSeek and Zhipu AI join OpenAI and Anthropic in developing custom inference chips, signaling a structural shift where leading AI labs pursue in-house silicon to cut costs and re...

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scmp.com /2 weeks ago

DeepSeek hiring spree: Chinese AI firm seeks newcomers as it pursues AGI

DeepSeek is looking to “at least double the size of every department” in a new hiring spree, according to an announcement made by the Chinese AI champion on Thursday. The firm anno...

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pandaily.com /1 week ago

Ant Group's LingBot-Vision Claims 12 World Firsts: 1.1B Parameter Model Beats 7B DINOv3

Ant Group's LingBot-Depth 2.0 spatial perception model achieves 12 world-first benchmarks, with its 1.1B parameter LingBot-Vision foundation model surpassing Meta's 7B DINOv3.

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dev.to

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