January 2023

Feeling like the wonders of AI are going to take away my job Wait a minute to learn a different skill There is work to be done before that ・What jobs will be taken away by AI? ・What can't AI do? ・ What if you lose your job due to AI Are you worried about these things? Worried about this ・ Acquire new skills ・ Changing jobs ・ Obtaining qualifications Everyone is only concerned about these hard skills. It's hard…

Epoch 1/100 83/83 [==============================] – 19s 164ms/step – loss: 0.0022 – mae: 0.0293 – acc: 3.7864e-04 Epoch 2/100 83/83 [==============================] – 14s 171ms/step – loss: 7.5855e-04 – mae: 0.0168 – acc: 3.7864e-04 Epoch 3/100 83/83 [==============================] – 15s 183ms/step – loss: 6.4637e-04 – mae: 0.0156 – acc: 3.7864e-04 Epoch 4/100 83/83 [==============================] – 15s 176ms/step – loss: 6.5974e-04 – mae: 0.0156 – acc: 3.7864e-04 Epoch 5/100 83/83 [==============================] – 15s 178ms/step – loss: 5.6503e-04 – mae: 0.0149 – acc: 3.7864e-04…

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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…

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…

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)…