When I was in the high school learning about AP statistics I learned the formula: $$ P(A|B)=\frac{P(A\cap B)}{P(B)} , P(B|A)=\frac{P(A\cap B)}{P(A)}$$ Which able to be transformed as: $$ P(A\cap B)=P(A)\cdot P(B|A)=P(B)\cdot P(A|B) $$ $P(A|B)$ is called “Conditional probability” which pretty much self-explained itself. For which I only knew the meaning of each element but not the whole idea, what I do is just plug in numbers, because it is kinda abstract to understand from itself: “The probability of event $A$ happens given event $B$ happened = The probability of events $A and B$ happens divided by the probability of event $B$ happens”

My dear teachers and parents, my lovely fellow class of 2020, it’s my great honor here to speak as a student representative, and thank you for choosing to join today’s graduation ceremony. You know, it might be the last time you see most of your surroundings, your classmates, your teacher. I mean, you would not even get in touch with them anymore. Maybe in the near future, you would call them, you would Wechat them, but as long as your life track does not match with them, then eventually you guys would go apart.

Lately, I was into the studying process of machine learning, and outputting(taking notes) is a vital step of it. Here, I am using Andrew Ng’s Stanford Machine Learning course in Coursera with the language of MATLAB. So the rest of the code I will write in this post by default are based on MATLAB. What is ML? “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

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It’s summer night, hot, but cold inside. The castle is full of people—women and knights. At that time, I could be the youngest one there. “What is a real knight?” the King Arthur suddenly looking at me with his deep green eyes. I did not except that, but as quick as it is my nature, I response words that I called out for thousands of times when I was an initiate knight: “Honor, wisdom, humble, never afraid!

Introduction AI face change is a product under Neuro Network and Machine Learning technologies. And this article aims to be a tutorial that could let people who do not have experience in Machine Learning to replace the face in one video with other faces. Key Words: #deepfakes #faceswap #face-swap #deep-learning #deeplearning #deep-neural-networks #deepface #deep-face-swap #fakeapp #fake-app #neural-networks #neural-nets What is DeepFaceLab? DeepFaceLab is a tool that utilizes machine learning to replace faces in videos.

Life, should be as perfect and harmony as a sphere which has the philosophical closeness and symmetry. Naturally, I keep this obsession into science, then I found I was wrong, on both. It seems like it was difficult to show my feelings to my parents, and because of it I tend to absorb information more than expressing it. Back to the time, the most frequently used body parts of mine to interact with the world are brain, eyes, and ears, the nose is not included because of my rhinitis.

Abstract This paper begins with the Messier 42 nebula, talking about how different color lights form due to the electron transport in Bohr’s model. How these lights are absorbed by gas and form the absorption spectrum, then how these lights are captured by sensors with the explanation between CCD and CMOS sensors. The history of spectrometer also includes plus the motivation and the reason why people are keeping doing that.

Inside the wide, white house, the “master” dressed in black, stands upon the platform. “Let’s celebrate the coming of our friends, shall we?” he says to the children before him. He is their father, but not by blood. The children, dressed in white, reply “yes” one by one, with planned perfect meticulous smiles. “Sorry, sir, but what are we celebrating?”, the youngest little “stander,” said. The “master” askance at him with unknown mean.

Boys, set your calculator to polar mode, and draw a graph of $r=4sin(3θ)$, then set the first interval of [0,π] and the second one: [0, 2π] [0,π] [0,2π] They looks exactly the same, isn’t it? And if we take of the integral of these two to find the area, for the graph with interval [0,π], area = 4π, but for the graph with [0,2π] the area is 8π, thus, which one is the “true” one we want to find?