Defined $I={1,2,…,N}$ is the set of goods. When the weight of each item $i in I$ is $w_i$ value and the upper limit of the total weight of $ v_i$ item is $W$, the following is called the knapsack problem. $$max ∑{i in I}v_iw_i s.t. ∑{i in I}v_iw_ile Wxin mathbb{N}(forall in I)$$ where $x_i$ represents the number of items to be placed in the knapsack. solution The problem is that if you perform a total search, you will try two…

Learn with books 1. Deep Learning (Adaptive Computation and Machine Learning series) This deep learning book is a resource for people involved in machine learning, by people involved in machine learning, to start from scratch for getting involved in machine learning. Provided by Ian Goodfellow, Yoshua Bengio, Aaron Courville What you will learn Configuration Part 1: Fundamentals of Applied Mathematics and Machine Learning Part 2: Modern Practical Deep Networks Part 3: Deep Learning Research *Linear factor model 2. Neural Networks…

Learn with books 1. Deep Learning (Adaptive Computation and Machine Learning series) This deep learning book is a resource for people involved in machine learning, by people involved in machine learning, to start from scratch for getting involved in machine learning. Provided by Ian Goodfellow, Yoshua Bengio, Aaron Courville What you will learn Configuration Part 1: Fundamentals of Applied Mathematics and Machine Learning Part 2: Modern Practical Deep Networks Part 3: Deep Learning Research *Linear factor model 2. Neural Networks…

$$QFT |x> = frac{1}{2^{n/2}} sum_{y=0}^{2^n-1} e^{2 pi iyx / 2^n} |y>$$ $|x_1>$ = -H-CR2-CR3-.. -CRn—————–$|x_2>$ = ——————H-CR2–.. ———-.. $|x_{n-1}>$ = ———————.. –H-CR2—$|x_n>$ = ————————-.. ——–H-

Perceptron Source code Learn more about perceptrons Import. Create data. Plot. Encode the discriminating function $$y(x) = f(w^T phi (x))$$ Code the activation function $$f(a) =begin{cases}+1 (a geq 0) \-1 (a leq 0)end{cases}$$ Predict. Code parameter updates $$w^{i+1} = w^{i} + eta phi (n) t(n)$$ Learn. Least squares Source code Learn more about least squares Import. Create data. It is the law of springs. Encoding two-section sum errors $$MSE = sum_{i=1}{n} (y_i – f(x_i))^2$$ Encode the least squares (regression line)…

Click here for the press release summary On October 4, 2022, the Nobel Prize in Physics was decided as follows: Alain Aspe Paris Sakurai University and École Polytechnique, Palaiseau, France John Crowther J.F. Crowther & Associates, Walnut Creek, California, USA Anton Seillinger Austria, University of Vienna They conducted groundbreaking experiments on "quantum entanglement" and their results paved the way from theory to technology. History and background of "quantum entanglement" The inexplicable effects of quantum mechanics are beginning to be applied.…

How to create a "soft sensor" based on an LSTM network You'll need to take the following steps: Collect data and preprocess It collects data from processes and systems that you want to monitor. It is to preprocess it for use in an LSTM network. This includes data cleaning, normalization, and network understanding. Format and more. Define the architecture of the LSTM network This is the number of layers, the number of neurons in each layer and other hyperparameters to…

The Age of Concepts We have already come to a society where eccentricity, novelty, play, and concepts are required rather than basic or complex designs. Even in the IT industry, if you use the shoulders of giants or frameworks, even ordinary people can do anything. That's why I feel that the people who can produce it are more valuable. What direction are we heading? From "function" to "design" From "discussion" to "story" From "individual" to "harmony of the whole" From…

Learn with books 1. Deep Learning (Adaptive Computation and Machine Learning series) This deep learning book is a resource for people involved in machine learning, by people involved in machine learning, to start from scratch for getting involved in machine learning. Provided by Ian Goodfellow, Yoshua Bengio, Aaron Courville What you will learn Configuration Part 1: Fundamentals of Applied Mathematics and Machine Learning Part 2: Modern Practical Deep Networks Part 3: Deep Learning Research *Linear factor model 2. Neural Networks…