Full bladder VS Elevator… 😮‍💨💦 👉 Jade Stone #peeaccident... #selfwetting #peedherselfshow more

260614 KMS #리쿠 #リク 롤러코스터 vs 귀신의 집 아... 둘다싫어요.. 😮‍💨💦💦 귀신의집..? (귀신) 진짜 있을 거 같은데 ..show more

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✍️ #OTD in 1783, Princess Yekaterina Dashkova shocked the... Russian Academy by introducing a new letter - "Ё". When scholars asked how it should sound, she (reportedly) went full Kabuki meme: “Yoooo!” 👉 That’s how #Russia got its most expressive letter & the eternal Е vs Ё war.show more

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Seedance V2 This prompt can be used for any... fight. I used the last two images I created with Nano banana Pro, not even a character sheet, someone could even open a new account with this prompt. The possibilities are endless. setting: location: "Ancient 'World Martial Arts Tournament' arena [@ Image 2]" details: "Clear stone platform textures, intricate Chinese guardian beast carvings, detailed ancient architecture" audio_style: "Shaw Brothers classic kung fu cinema soundtrack" action_sequence: participants: "[@ Image 1] vs [@ Image 3], both unarmed/bare-handed" choreography: opening: "[@ Image 1] moves like lightning with sharp energy-infused strikes; [@ Image 3] parries using fluid Tai Chi grandmaster techniques to neutralize the onslaught." climax: "[@ Image 1] lunges for a tail-whip ambush; [@ Image 3] counters with a powerful qi-palmed strike. [@ Image 1] dodges with ghost-like agility." finisher: "[@ Image 1] fires a Kamehameha at the chest; [@ Image 3] tanks the hit with a qi-shield and counters with a full-force palm strike, knocking [@ Image 1] off the ring." cinematography: camera: "360-degree orbital wrap-around shots, capturing every martial arts exchange" lighting: "Dynamic lighting shifts synced with combat intensity to create a tense atmosphere" visual_style: "Cinematic photorealism, 8K resolution, film-like texture" technical_quality: standard: "Low AI signature, no excessive skin smoothing, natural fluid motion" negative_constraints: "No deformed limbs, no extra/missing fingers, no clipping, no blurring, no low resolution, no cluttered backgrounds, no color banding"show more

Full Fine-tuning vs. Freezing Layers. Interact 👉 and ==... Full Fine-tuning == A real network has many — three layers in this example, billions of parameters in a production model. What does fine-tuning look like when you update all of them? That’s full fine-tuning: continue training every weight in the pretrained network on your new task. Every layer’s W gets its own ΔW. Nothing is frozen — every parameter is in play. Think of an MLP as a chain of prerequisites leading to an advanced course. Layer 1 might be Linear Algebra, layer 2 Probability, layer 3 Advanced Machine Learning — each one building on what came before. Fine-tuning is what happens during graduate study: the foundations are already there from undergrad, so you’re not re-learning. Full fine-tuning is reviewing every prerequisite to see what new topics have appeared and what discoveries the field has made since the last time you sat through them. Effective — but exhausting. This diagram shows the same three-layer MLP twice, side by side. On the left, the pretrained network runs on input X: three weight matrices W₁, W₂, W₃, each followed by a ReLU activation. Full fine-tuning gives the model the most freedom to specialize. Every parameter can move — and every parameter that can move must be stored. But not every prerequisite needs revisiting. The further you go back in the chain, the less the material has changed since pretraining — the linear-algebra basics under your computer-vision course are largely the same as they ever were. The next page does exactly that: freeze the prerequisites that haven’t moved, and only refresh the advanced one closest to your specialization. == Freezing Layers == Full fine-tuning reviewed every prerequisite — Linear Algebra, Probability, Advanced ML — to refresh each subject with the latest topics. Effective, but exhausting. Then you realize something. The prerequisites haven’t actually changed that much. Linear Algebra is still Linear Algebra; the matrix decompositions you learned still hold. Probability is still Probability; the distributions and Bayes’ rule haven’t moved. Almost all the new material — the new ideas, the recent discoveries — lives in the advanced layer at the top. That’s freezing layers: keep the prerequisite layers fixed at their pretrained state, and only update the advanced one. In the diagram below, W1​ and W2​ — the foundational prerequisites — stay frozen. Only W3​ — the layer closest to your task-specific output — gets a ΔW.show more