A New Life

Week 3: Using RNNs to predict time series Welcome! In the previous assignment you used a vanilla deep neural network to create forecasts for generated time series. This time you will be using Tensorflow's layers for processing sequence data such as Recurrent layers or LSTMs to see how these two approaches compare. Let's get started! import tensorflow as tf import numpy as np import matplotlib.py..

Week 2: Predicting time series Welcome! In the previous assignment you got some exposure to working with time series data, but you didn't use machine learning techniques for your forecasts. This week you will be using a deep neural network to create forecasts to see how this technique compares with the ones you already tried out. Once again all of the data is going to be generated. Let's get sta..

Week 1: Working with time series Welcome! In this assignment you will be working with time series data. All of the data is going to be generated and you will implement several functions to split the data, create forecasts and evaluate the quality of those forecasts. Let's get started! import numpy as np import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt The next..

1. [27433] 팩토리얼 2 0보다 크거나 같은 정수 N이 주어진다. 이때, N!을 출력하는 프로그램을 작성하시오. def factorial(n): if n > 0: return n * factorial(n-1) else: return 1 N = int(input()) print(factorial(N)) 2. [10870] 피보나치 수 5 피보나치 수는 0과 1로 시작한다. 0번째 피보나치 수는 0이고, 1번째 피보나치 수는 1이다. 그 다음 2번째 부터는 바로 앞 두 피보나치 수의 합이 된다. 이를 식으로 써보면 Fn = Fn-1 + Fn-2 (n ≥ 2)가 된다. n=17일때 까지 피보나치 수를 써보면 다음과 같다. 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144,..

1. [18258] 큐 2 정수를 저장하는 큐를 구현한 다음, 입력으로 주어지는 명령을 처리하는 프로그램을 작성하시오. 명령은 총 여섯 가지이다. push X: 정수 X를 큐에 넣는 연산이다. pop: 큐에서 가장 앞에 있는 정수를 빼고, 그 수를 출력한다. 만약 큐에 들어있는 정수가 없는 경우에는 -1을 출력한다. size: 큐에 들어있는 정수의 개수를 출력한다. empty: 큐가 비어있으면 1, 아니면 0을 출력한다. front: 큐의 가장 앞에 있는 정수를 출력한다. 만약 큐에 들어있는 정수가 없는 경우에는 -1을 출력한다. back: 큐의 가장 뒤에 있는 정수를 출력한다. 만약 큐에 들어있는 정수가 없는 경우에는 -1을 출력한다. from collections import deque import ..

Week 4: Predicting the next word Welcome to this assignment! During this week you saw how to create a model that will predict the next word in a text sequence, now you will implement such model and train it using a corpus of Shakespeare's sonnets, while also creating some helper functions to pre-process the data. Let's get started! NOTE: To prevent errors from the autograder, please avoid editin..

Week 3: Exploring Overfitting in NLP Welcome to this assignment! During this week you saw different ways to handle sequence-like data. You saw how some Keras' layers such as GRU, Conv and LSTM can be used to tackle problems in this space. Now you will put this knowledge into practice by creating a model architecture that does not overfit. For this assignment you will be using a variation of the ..

Week 2: Diving deeper into the BBC News archive Welcome! In this assignment you will be revisiting the BBC News Classification Dataset, which contains 2225 examples of news articles with their respective labels. This time you will not only work with the tokenization process but you will also create a classifier using specialized layers for text data such as Embedding and GlobalAveragePooling1D. ..

Week 1: Explore the BBC News archive Welcome! In this assignment you will be working with a variation of the BBC News Classification Dataset, which contains 2225 examples of news articles with their respective categories (labels). Let's get started! import csv from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences Begin by look..

Week 4: Multi-class Classification Welcome to this assignment! In this exercise, you will get a chance to work on a multi-class classification problem. You will be using the Sign Language MNIST dataset, which contains 28x28 images of hands depicting the 26 letters of the english alphabet. You will need to pre-process the data so that it can be fed into your convolutional neural network to correc..

Week 3: Transfer Learning Welcome to this assignment! This week, you are going to use a technique called Transfer Learning in which you utilize an already trained network to help you solve a similar problem to the one it was originally trained to solve. Let's get started! NOTE: To prevent errors from the autograder, please avoid editing or deleting non-graded cells in this notebook . Please only..

Week 2: Tackle Overfitting with Data Augmentation Welcome to this assignment! As in the previous week, you will be using the famous cats vs dogs dataset to train a model that can classify images of dogs from images of cats. For this, you will create your own Convolutional Neural Network in Tensorflow and leverage Keras' image preprocessing utilities, more so this time around since Keras provides..