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"Voice gets interesting the moment developers find interaction patterns that only voice makes possible." - @scottcjohnston We built a @DeepLearningAI course on exactly that. Voice for AI Agents and Applications, with @_ashwyn , @AndrewYNg , and @scottcjohnston. Most voice infrastructure out there was built for one job: replace humans in contact centers. Answer the phone, follow the script, close the ticket. Building voice into a real product is a different problem. This free short course is about that, giving your apps and agents an actual voice. You'll walk away having built: 🎮 a voice-interactive game where voice and mouse clicks run over one channel 🤖 a chat agent that gains a voice in ~10 lines, no touching your prompts, RAG, or tools 📞 an agent that makes real outbound phone calls and streams the transcript back live Enroll for free: We'd love to see what you build. Let's go 🚀

The future of humanoids isn't just mechanical—it's neural. A humanoid robot is an embodied AI system. Motors, sensors, and actuators provide the body, but neural intelligence provides the mind. Neural models power vision, speech, language, navigation, manipulation, planning, memory, decision-making, and continuous learning. As robotics evolves, nearly every cognitive subsystem is becoming neural-first. That's why represents more than a niche—it's a foundational concept for the next generation of intelligent robotics. #HumanoidRobots# #EmbodiedAI# #PhysicalAI# #NeuralNetworks# #ArtificialIntelligence# #Robotics# #MachineLearning# #DeepLearning# #RobotLearning# #ReinforcementLearning# #ComputerVision# #GenerativeAI# #AIResearch# #Automation# #FutureOfAI#

直接从顶级公司免费学习 AI 1 - Anthropic: 2 - Google: 3 - Meta: 4 - NVIDIA: 5 - Microsoft: 6 - OpenAI: 7 - IBM: 8 - AWS: 9 - DeepLearning AI: 10 - Hugging Face:

老外这个骚操作也是相当邪门，这博主把吴恩达/DeepLearning.AI 的几段内容拼在一起、配了时间戳，然后就说吴恩达刚发布了一个 3 小时的课程，后面配自己的长文……然后一堆人收藏点赞 😂

2026 年学习 AI 最值得关注的 YouTube 频道，拒绝废话版。 收藏起来，按这个顺序学： 1. 3Blue1Brown AI / 数学基础。用可视化方式讲清楚线性代数、神经网络和底层数学直觉。 2. Andrej Karpathy 深度学习 / LLM。前 OpenAI、Tesla AI 核心人物，讲课硬核但非常清晰。 3. Yannic Kilcher AI 研究。适合跟进论文、模型架构和前沿研究动态。 3. AssemblyAI 实用 AI。大量语音识别、LLM、AI 工程化和 API 实战内容。 4. AI Explained LLM / AI 趋势。适合理解大模型能力边界、行业变化和最新进展。 5. StatQuest 机器学习理论。把统计学、机器学习算法讲得非常通俗。 6. Two Minute Papers 论文简明讲解。用短视频快速了解 AI、图形学和科研新成果。 7. Matthew Berman 生成式 AI。关注 AI 工具、开源模型、LLM 应用和最新产品。 8. Nicholas Renotte AI Agents / 实战项目。适合想动手做项目、Agent、自动化和计算机视觉的人。 9. Krish Naik 应用机器学习。数据科学、机器学习、MLOps 和实战项目内容很多。 10. Aladdin Persson PyTorch / 深度学习代码。适合系统学习 PyTorch、CNN、GAN、Transformer 等实现。 11. Serrano Academy 机器学习数学。适合补机器学习背后的数学、概率和算法直觉。 12. Lex Fridman 行业洞察。通过长访谈理解 AI、科技、创业、机器人和社会影响。 13. DeepLearningAI 真实世界 AI。Andrew Ng 团队出品，适合系统学习 AI 工程和应用落地。 我的建议： 新手先看 3Blue1Brown + StatQuest + DeepLearningAI。 想做工程项目，看 Karpathy + Nicholas Renotte + Krish Naik。 想跟前沿趋势，看 Yannic Kilcher + AI Explained + Matthew Berman。 别只收藏。 真正的学习路径是： 看一集，记一页笔记，复现一个小项目。 你也可以把最后一句改成更有传播感的一版： AI 学习最怕的不是资源不够，而是收藏太多、动手太少。

公司领导 + AI = ？ 有这么一些公司领导，他们在一些领域并不专业，甚至不懂，但就是心里觉得这个事情很简单，做不好是人的问题！ 这种领导，加上 AI，都不用 Claude Fable 5 或 GPT-5.5，豆包就行，他们在你提出方案时，会直接用 AI 去查，然后跟你说: 我问了 AI，很简单啊，根本没有你说的那么复杂，然后你就陷入了自证的阶段 😂 不是说不懂的公司领导不能用 AI，关键是，您把上下文输对啊，含含糊糊的问一句，而且带着预设倾向，AI 回复的是最接近简单场景的做法，并不解决实际复杂场景。 比如，为什么有了 OpenCV，还需要 CV 领域的 Deeplearning？是的，就是这么明显的问题，在前司甚至很多公司都在反复出现。。。

15 AI related accounts you should follow on Twitter: 1. @karpathy His tweets already create LLMs narratives that you later see on linkedin in 2 months. 2. @fchollet posts thoughtful research on intelligence, benchmarks, and AI limitations. Keras creator + ARC-AGI 3. @ylecun Yann LeCun is Deep learning pioneer & Meta Chief AI Scientist; big-picture research takes and critiques (and drama). 4. @AndrewYNg Andrew Ng is AI education legend; practical ML advice, courses, and real-world implementation. creator of deeplearning ai 5. @rasbt Sebastian Raschka posts on Practical ML/LLM implementations, "build from scratch" tutorials, and books. 6. @dair_ai Weekly ML/AI paper threads and accessible research explainers (high-signal for staying current). 7. @lilianweng Lilian Weng is ex-OpenAI, and her Lil'Log-style threads are good. has In-depth LLM research breakdowns 8. @jeremyphoward posts interesting takes on AI/crypto news, and works on democratizing practical deep learning and accessible education. 9. @simonw Simon posts Practical LLM tools, takes, experiments, prompting, and engineering breakdowns. django co-founder 10. @_akhaliq Curates the latest arXiv papers, model releases, and open-source AI drops. 11. @ID_AA_Carmack AGI/low-level optimization takes that makes you think about the problem. 12. @gwern Really high-quality long-form AI research notes and essays. 13. @goodside LLM evaluation, prompting research, and real capabilities testing 14. @drfeifei Computer vision pioneer; human-centered AI and spatial intelligence research 15. @demishassabis Been following his work for 9 years. Demmis is my hope against google usurpating their power with AI. Demmis is Google DeepMind's CEO Let me know who I missed, guys, and save it for the future

2026年学习AI的最佳YouTube频道： 1. AI Explained 👉 2. Andrej Karpathy 👉 3. Cole Medin 👉 4. DeepLearningAI 👉 5. Futurepedia 👉 6. Matthew Berman 👉 7. Skill Leap AI 👉 8. Tech With Tim 👉 9. Tina Huang 👉 10. Two Minute Papers 👉

Backpropagation by hand ✍️ ~ 11 steps walkthrough below Backpropagation is the algorithm that actually trains a neural network, and it is where most people stop following along. It is not calculus you cannot do. It is matrix multiplication, working backward, one layer at a time. So I drew and calculated one entirely by hand. Goal: push the loss gradient back through a 3-layer network and land on a new value for every weight and bias. = 1. Given = A 3-layer perceptron, an input X, predictions Ypred = [0.5, 0.5, 0], and the truth Ytarget = [0, 1, 0]. = 2. Backprop gradient cells = Let us draw empty cells for every gradient we are about to compute. The shape of the answer comes first. = 3. Layer 3 softmax = We get dL/dz3 straight from Ypred minus Ytarget = [0.5, -0.5, 0]. No chain rule needed, and that shortcut is the whole reason softmax and cross-entropy are paired. = 4. Layer 3 weights and biases = Let us multiply dL/dz3 by [a2 | 1]. One multiplication gives the gradient for W3 and b3 together. = 5. Layer 2 activations = We multiply dL/dz3 by W3 to get dL/da2. The gradient moves back across a layer the same way the signal moved forward. = 6. Layer 2 ReLU = Let us pass it through the gate: keep the gradient where the activation was positive, zero it everywhere else. = 7. Layer 2 weights and biases = We multiply dL/dz2 by [a1 | 1]. The same figure as step 4, one layer up. = 8. Layer 1 activations = Let us multiply dL/dz2 by W2. = 9. Layer 1 ReLU = We apply the same gate again, now on a1. = 10. Layer 1 weights and biases = Let us multiply dL/dz1 by [x | 1], and every weight in the network now has a gradient. = 11. Update = We subtract, and the network has learned. In practice a learning rate scales this step. The gradients: dL/dz3 = [0.5, -0.5, 0] dL/da1 = [1, -2, 2, -1] dL/dz1 = [0, -2, 2, -1] The takeaway: matrix multiplication is all you need. Just like the forward pass, backpropagation is matrix multiplications end to end. You can do every one by hand, slowly and imperfectly, which is exactly why a GPU's ability to do them fast mattered so much to deep learning. 💾 Save this post!

当 AI 和世界还没有今天这么喧闹的时候，一个名叫 Andrej Karpathy 的年轻人，戴着那时还很时髦的 Google Glass，骑着自行车，从他上学的斯坦福，去他实习的 Google，一边骑，一边录了一段 vlog。 视频拍于 2013 年。那时他还是斯坦福博士生，在 Google 实习，做的项目是 deep learning：搭神经网络，让它理解图片和视频。拍摄工具是 Google Glass。他还自己给它写了应用，出门就戴着。他最喜欢的游戏是玩魔方，其实是他休息的方式。 斯坦福 计算机系的走廊，是 Larry Page 和 Sergey Brin 当年读博时走过的同一条。第一台 Google 服务器，也曾在这栋楼的地下室运行过。 Palo Alto 的路，到今天还是他当年记录的那个样子，破得很稳定。加州人有钱，但就是不用来修路。该咋地咋地。 到了 Google，园区还是那种这家公司刚刚长出来的感觉：餐厅、共享自行车、来参观的游客，还有一头恐龙雕塑。那时的 Google 还在三驾马车（Eric Schmidt带着两个founder）年代，除了 Google Glass，还在做 Google 气球和自动驾驶。 那个年代好像已经很远了。 当年连 Andrej 都要辛苦搭起来的神经网络，现在应该已经很容易了吧。 如果你对那个年代的斯坦福、Google 和 Palo Alto 好奇，这条视频是一个稀有的窗口。