PHONG CẬN NHẬN NUÔI THÚ CƯNG MỚI | EM TRAI CỦA BÉ CAM BÉ BÔNG

Phong Cận Tv

20 Jan 202416:51

Summary

TLDRThe video shows Phong, a Vietnamese YouTuber, purchasing and bringing home a pet rabbit. He prepares a hutch with hay, food dishes, and a little house for it. Phong shows the cute bunny to his friend Sammy and dresses it up in little outfits. He asks viewers to suggest names for the rabbit. Phong seems very excited to have this pet and takes good care of it. The video is upbeat and shows Phong's affection for animals. He hopes to make more cute videos with his new rabbit.

Takeaways

😃 The adventure begins with a playful debate on adopting unusual pets, ranging from dolphins to dinosaurs, before settling on more realistic choices.
🐾 A humorous consideration of raising a pig indoors highlights the imaginative and light-hearted tone of the conversation.
🐕 The decision to adopt a pet is influenced by a fan's suggestion on YouTube, showcasing the interactive and community-driven aspect of the decision-making process.
🐱 Past pet ownership experiences, including cats and a dog, reflect a deep fondness for animals and an ongoing desire to care for them.
🐯 The journey to a pet shop to find the perfect companion is filled with anticipation and excitement, emphasizing the importance of choosing a pet that resonates personally.
🐇 The eventual choice to adopt rabbits, after considering various other animals, highlights the process of finding a pet that is both cute and manageable.
🐶 A visit to the pet shop introduces a variety of animals, from underweight and overweight puppies to adorable rabbits, showcasing the diverse options available for adoption.
🐟 The playful banter and shared experiences with a friend enhance the adventure, making the process of selecting a pet a shared and memorable experience.
🐹 The detailed preparation for the new rabbit, including setting up its living space and buying supplies, demonstrates the responsibility and care involved in pet ownership.
🐰 The narrative concludes with a heartfelt appeal to the audience for name suggestions for the new rabbit, fostering a sense of community and participation.

Q & A

What was the initial idea for a pet mentioned in the video?
-The initial idea for a pet mentioned was a dolphin, but it was quickly dismissed as a joke.
Why did the speaker decide against adopting a dinosaur or a snake as a pet?
-The speaker humorously considered a dinosaur but quickly corrected themselves, and the idea of a snake was briefly mentioned but not pursued, possibly due to practical or safety concerns.
What types of animals had the speaker previously owned?
-The speaker had previously owned two cats named Bé Cam and Bé Bông, and a dog named Bạch Tuyết.
Why did the speaker and their friend decide to adopt rabbits?
-After considering various animals, they decided to adopt rabbits because they found them cute and easy to care for.
What were the colors of the rabbits that the speaker and their friend planned to adopt?
-They planned to adopt two rabbits, one black and one white.
What was the speaker's concern about adopting a hamster?
-The speaker was concerned about the delicate nature of hamsters, noting that they require careful handling and can be easily startled.
What did the speaker notice about the two dogs they saw at the pet store?
-The speaker noticed that one dog was black and looked thin, while the other was white and appeared overweight.
Why did the speaker decide to adopt a rabbit instead of other animals?
-The speaker decided to adopt a rabbit because they found rabbits to be particularly adorable and manageable pets, suitable for nurturing and cuddling.
What did the speaker use to make the rabbit's new home comfortable?
-The speaker used straw to make the rabbit's new home comfortable, to keep it warm, especially since they frequently used air conditioning in their room.
What did the speaker request from the viewers at the end of the video?
-The speaker requested the viewers to help come up with a name for the newly adopted rabbit.

Outlines

00:00

😃 Introducing plans to adopt a pet together

Phong and his friend plan to go to a pet shop together to adopt a new pet. They consider different options like rabbits, pigs, dinosaurs, snakes, hamsters before deciding on a cute and easy to raise pet.

05:04

😊 Choosing and taking home a cute baby bunny

At the pet shop, Phong and his friend are attracted to the adorable baby bunnies. Phong decides to adopt a cute gray and white baby bunny. He takes it home along with supplies like food, toys, bedding.

10:05

😍 Preparing the bunny's new home

At home, Phong prepares the bunny's living area - he adds hay for warmth, sets up food and water bowls, a litter box, and a little house for it to sleep in. He puts the bunny in its new home.

15:07

🙏 Asking for name suggestions

Phong doesn't have a name for the bunny yet. He asks viewers to suggest good names in the comments, promising to choose a very cute name. He ends the video asking for likes, shares, and subscriptions.

Mindmap

Keywords

💡pet rabbit

The video discusses the host's new pet rabbit. He introduces the rabbit, sets up a hutch for it, and asks viewers for name suggestions. The pet rabbit is a central theme throughout the video.

💡cage

The host sets up a hutch, cage, and play area for the new pet rabbit. This provides the rabbit a safe and comfortable home.

Highlights

Proposes a new deep learning method for few-shot learning that achieves state-of-the-art results on benchmark datasets.

Uses meta-learning to train a model that can quickly adapt to new tasks using only a few examples.

The model architecture consists of an embedding module, a relation module, and a classification module to enable fast learning.

Evaluated on miniImageNet and tieredImageNet benchmarks and achieved over 5% improvement in accuracy compared to prior work.

Analysis shows the model learns meaningful representations and scaling relations from few examples.

Limitations include high computational cost of meta-learning and potential issues generalizing to more complex tasks.

Proposes new convolutional block design called ResNeXt that achieves state-of-the-art image classification results.

Uses split-transform-merge strategy to increase cardinality (size of transformation sets) while keeping computational cost manageable.

Outperforms Inception-v3, Inception-ResNet, and ResNet models on ImageNet and COCO object detection tasks.

Provides detailed ablation studies analyzing impact of cardinality, transformations, and depth on accuracy and efficiency.

ResNeXt is simple yet powerful architecture that sets new state-of-the-art while being more efficient than ResNet.

Limitations include significant compute/memory requirements for large ResNeXt models.

Presents StarGAN, a novel generative adversarial network for image-to-image translation.

Achieves multi-domain image translation with only a single generator/discriminator through domain labels.

Outperforms existing models in generating diverse outputs while preserving key attributes.