female X(s) = The hometown X(s) of a randomly drawn person (s) from (S) Examples: Continuous random variables (S is continuous) X(s) = r be the heart rate of a randomly drawn person s in our class S Feng valued random variable is a function of the outcome of a ran- domized experiment X : S → R For continuous random variable X P(a < X < b) = P({s ∈ S : a < X(s) < b}) For discrete random variable X P(X 9 / 122 Probability Distribution (Contd.) Probability distribution for continuous random variables Suppose X is a continuous random variable X : S → A Probability density function (PDF) of X is a

converting high precision continuous values to low precision discrete values. Take a look at figure 2-3. It shows a sine wave and an overlapped quantized sine wave. The sine wave is continuous, a high precision Figure 2-4: Quantizing floating-point continuous values to discrete unsigned values. The continuous values range from xmin to xmax, and are mapped to continuous values in [0, 2b - 1] (in the above figure = 3, hence the quantized values are in the range [0, 7]. For the purpose of quantization, the continuous values are also clamped to be in the range [xmin, xmax]. Solution: Note that we have to map all

Logistic Regression 主讲人：龙良曲 Recap ▪ for continuous: ? = ?? + ? ▪ for probability output: ? = ? ?? + ? ▪ ?: ??????? ?? ???????? Binary Classification ▪ interpret network as ?: ? → ? ? ?; ? ▪ output = 0 if accuracy unchanged but weights changed ▪ issues 2. gradient not continuous since the number of correct is not continuous Q2. why call logistic regression ▪ use sigmoid ▪ Controversial! ▪ MSE

of these choices are boolean, others have discrete parameters and still there are the ones with continuous parameters. Some choices even have multiple parameters. For example, horizontal flip is a boolean a set of well-defined values for each of those parameters. The parameters can take discrete or continuous values. It is called a "search" space because we are searching for a point in which minimizes (or example of a search space with two parameters. However, in this example, the second parameter is a continuous valued parameter with domain . are some of the valid points in this search space. As we can see

(y; ψ) = P(Y = y) = ψy(1 − ψ)1−y (5) • A2: X | Y = 0 ∼ N(µ0, Σ): The conditional probability of continuous random variable X given Y = 0 is a Gaussian distribution parameterized by µ0 and Σ, such that the � −1 2(x − µ0)T Σ−1(x − µ0) � (6) • A3: X | Y = 1 ∼ N(µ1, Σ): The conditional probability of continuous random variable X given Y = 1 is a Gaussian distribution parameterized by µ1 and Σ, such that the

情感分类实战 主讲人：龙良曲 Google CoLab ▪ Continuous 12 hours ▪ free K80 for GPU ▪ no need to cross GFW Load Dataset Network Load word embedding Train Test 下一课时 GAN Thank You.

Shandong University, China 1 Linear Regression Problem In regression problem, we aim at predicting a continuous target value given an input feature vector. We assume a n-dimensional feature vector is denoted

regression problem, but we would like to predict only a small number of discrete values (instead of continuous values) Binary classification problem: y ∈ {0, 1} where 0 represents negative class, while 1 denotes

Li (SDU) Linear Regression September 13, 2023 2 / 31 Supervised Learning Regression: Predict a continuous value Classification: Predict a discrete value, the class Living area (feet2) Price (1000$s)

(user/item/attribute)  动态图 标准化: Standard Libraries Graph-Learn: 分布式图算法库 标准化: Standard Solutions Continuous Optimization: Active learning Data Label Model Serving CV / NLP解决方案: EAS Web App Mobile