@metanoiametanoia_:

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Tuesday 01 September 2026 05:49:14 GMT

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Foam Concrete Troy :

Praise The Lord Jesus Christ

2026-09-01 08:47:29

0

siaw Lian khong :

bagi para hater.............apakah polisi juga bohong? yg pasti Kalo pendeta ini bohong, mungkin sdh diborgol dinaikan ke mobil tawanan

2026-09-01 06:46:07

1

KAROxRiee :

kalo gini gaperlu ke dokter mah

2026-09-01 08:57:25

0

Lie Lie Khin :

Amin puji tuhan

2026-09-01 05:58:17

0

Yo Kim An :

amin nysta mujizat tuhan yesus.

2026-09-01 08:22:38

0

WEMPy 💔💔🙏🫶 :

Amin 🙏🙏

2026-09-01 10:31:22

0

viggo Almonzo tuauni :

amen

2026-09-01 06:46:35

0

videlsrgr :

AMEN PUYI Tuhan' yesus 🙏

2026-09-01 06:49:57

0

Timotius :

2026-09-01 06:35:55

0

Pahala Manogi :

amin...

2026-09-01 07:25:20

0

MAROJAHAN SIAGIAN :

Amin

2026-09-01 06:22:39

0

Dominic sitorus :

amin

2026-09-01 05:53:15

0

bahar_12 :

amin🥰

2026-09-01 05:59:00

0

Maxy Adoe :

🥰

2026-09-01 07:14:10

0

Maxy Adoe :

🥰🥰

2026-09-01 07:14:04

0

kiko iko :

puji Tuhan Yesus Kristus memberkati kita semua

2026-09-01 08:49:04

0

To see more videos from user @metanoiametanoia_, please go to the Tikwm homepage.

Other Videos

#fyp #foryoupagе #viral #fyp

#fyp #foryoupagе #viral #fyp

Nước hoa ô tô thơm y như mùi ở khách sạn 5 sao luôn #nuochoa #nuochoaoto #khachsan5sao #tinhdauthomphong #kathwi @Cô 2 Oanh

Nước hoa ô tô thơm y như mùi ở khách sạn 5 sao luôn #nuochoa #nuochoaoto #khachsan5sao #tinhdauthomphong #kathwi @Cô 2 Oanh

The main difference between a junior developer and a senior developer is that junior developers focus on writing code to complete specific tasks, while senior developers focus on solving broader problems, designing scalable systems, and helping their teams succeed.Key DifferencesExperience and Independence:Junior: Usually has 0 to 2 years of experience. They need clear instructions, supervision, and guidance on how to complete tasks.Senior: Usually has 5 to 7+ years of experience. They handle high ambiguity, figure out unclear paths, and work with minimal direction.Approach to Coding:Junior: Focuses on making a specific feature work. They often use an optimistic approach and may not consider edge cases or performance.Senior: Focuses on the big picture, including security, scalability, performance, and long-term maintenance.Handling Problems:Junior: Solves isolated tasks.Senior: Understands the root cause of a problem and designs simple solutions for complex issues.Team Impact:Junior: Concentrates on learning the technology stack and growing their own skills.Senior: Mentors others, reviews code, sets coding standards, and helps the entire team become more productive.You can read more about the gap in mindset on Medium.If you want, let me know:Are you trying to prepare for an interview?Are you trying to write a job description?I can give you more specific details for your goal. #developer #senior

The main difference between a junior developer and a senior developer is that junior developers focus on writing code to complete specific tasks, while senior developers focus on solving broader problems, designing scalable systems, and helping their teams succeed.Key DifferencesExperience and Independence:Junior: Usually has 0 to 2 years of experience. They need clear instructions, supervision, and guidance on how to complete tasks.Senior: Usually has 5 to 7+ years of experience. They handle high ambiguity, figure out unclear paths, and work with minimal direction.Approach to Coding:Junior: Focuses on making a specific feature work. They often use an optimistic approach and may not consider edge cases or performance.Senior: Focuses on the big picture, including security, scalability, performance, and long-term maintenance.Handling Problems:Junior: Solves isolated tasks.Senior: Understands the root cause of a problem and designs simple solutions for complex issues.Team Impact:Junior: Concentrates on learning the technology stack and growing their own skills.Senior: Mentors others, reviews code, sets coding standards, and helps the entire team become more productive.You can read more about the gap in mindset on Medium.If you want, let me know:Are you trying to prepare for an interview?Are you trying to write a job description?I can give you more specific details for your goal. #developer #senior

😱 Future Shoes Are Here #tiktokshop #tiktokgrowthchallenge #ironman #futuristicfashion #trending

😱 Future Shoes Are Here #tiktokshop #tiktokgrowthchallenge #ironman #futuristicfashion #trending

#เตาแก๊ส #เตาแก๊ส2หัว #creatorsearchinsights #ของขายในtiktok #tiktokshop99วันไลฟ์แห่งชาติ

#เตาแก๊ส #เตาแก๊ส2หัว #creatorsearchinsights #ของขายในtiktok #tiktokshop99วันไลฟ์แห่งชาติ

In 90 seconds, you can learn a large portion of the math behind how neural networks actually work. It is a very simplified picture, but let me show you the actual math. Every neural network has three kinds of layers: an input layer, one or more hidden layers, and an output layer. Each layer is made of neurons (the dots), connected by edges. Every edge carries its own weight: a single number. Your word or text input goes through a transformation into number form to enter the AI model. That transformation is called an embedding. In real models an embedding can be hundreds of numbers long; here I will use just two. Say the input is 5 and 2. Now give each edge a weight. To reach one neuron in the hidden layer, two edges feed into it: one with weight 10, one with weight 2. The neuron's value is 5 x 10 + 2 x 2, which is 50 + 4, so 54 (remember PEMDAS, my friends). The neuron beside it has its own edges, say weights 20 and 4. Its value is 5 x 20 + 2 x 4, which is 100 + 8, so 108. Multiply each input by the weight on its edge, add the results, and pass the number forward. Do it again for the next layer, and the next. Basically every generative AI model you use runs on this. One thing we are missing here: the nonlinear functions. They are crucial to neural networks, and I can make another video on those. The output layer also works a little differently from the hidden layers, and I will cover that in Part 2. I can also break down how these weights are actually learned, through backpropagation. If any of that interests you, or if I can do better somehow, let me know in the comments. Welcome!! I'm Harper. I've spent a decade building AI as a Stanford computer scientist, and now I hope to share the intuition I've developed with you. For a complete AI basics walkthrough, check out the link in my bio for my (free) video walkthrough guide.

In 90 seconds, you can learn a large portion of the math behind how neural networks actually work. It is a very simplified picture, but let me show you the actual math. Every neural network has three kinds of layers: an input layer, one or more hidden layers, and an output layer. Each layer is made of neurons (the dots), connected by edges. Every edge carries its own weight: a single number. Your word or text input goes through a transformation into number form to enter the AI model. That transformation is called an embedding. In real models an embedding can be hundreds of numbers long; here I will use just two. Say the input is 5 and 2. Now give each edge a weight. To reach one neuron in the hidden layer, two edges feed into it: one with weight 10, one with weight 2. The neuron's value is 5 x 10 + 2 x 2, which is 50 + 4, so 54 (remember PEMDAS, my friends). The neuron beside it has its own edges, say weights 20 and 4. Its value is 5 x 20 + 2 x 4, which is 100 + 8, so 108. Multiply each input by the weight on its edge, add the results, and pass the number forward. Do it again for the next layer, and the next. Basically every generative AI model you use runs on this. One thing we are missing here: the nonlinear functions. They are crucial to neural networks, and I can make another video on those. The output layer also works a little differently from the hidden layers, and I will cover that in Part 2. I can also break down how these weights are actually learned, through backpropagation. If any of that interests you, or if I can do better somehow, let me know in the comments. Welcome!! I'm Harper. I've spent a decade building AI as a Stanford computer scientist, and now I hope to share the intuition I've developed with you. For a complete AI basics walkthrough, check out the link in my bio for my (free) video walkthrough guide.

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