Assessed Exercise for Deep Learning (M)¶ This exercise must be submitted as a colab notebook. Deadline Monday the 4th of March, 15:00. You will create a classifier and test it on a collection of images for a new task. While you are welcome to build a full network from scratch, most of you will not […]
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Deep Learning and Text Analytics II ¶ References: • General introduction ▪ http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/ • Word vector: ▪ https://code.google.com/archive/p/word2vec/ • Keras tutorial ▪ https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/ • CNN ▪ http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/ 1. Agenda¶ • Introduction to neural networks • Word/Document Vectors (vector representation of words/phrases/paragraphs) • Convolutional neural network (CNN) • Application of CNN in text classification 4. Word2Vector
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MNIST classification using LBP feature and SVM¶ Step 1: Import required packages¶ In [0]: import numpy as np import matplotlib.pyplot as plt from skimage import feature # This pacakge is used for LBP feature extraction from sklearn import svm # This pacakge is used for svm classification from sklearn import neighbors # this package is used
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Sentiment Classification: classifying IMDB reviews¶ In this task, you will learn how to process text data and how to train neural networks with limited input text data using pre-trained embeddings for sentiment classification (classifying a review document as “positive” or “negative” based solely on the text content of the review). We will use the Embedding
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Deep Learning Assignemnt 1 1.Preparation¶ 1.1Import required packages¶ In [0]: import pandas as pd import numpy as np import matplotlib.pyplot as plt import cv2 import seaborn as sns from skimage import feature from sklearn import svm from sklearn.neighbors import KNeighborsClassifier from sklearn.utils import shuffle from sklearn import metrics from sklearn.metrics import accuracy_score from sklearn.model_selection import cross_val_score
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Deep Learning and Text Analytics ¶ References: • General introduction ▪ http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/ • Word vector: ▪ https://code.google.com/archive/p/word2vec/ • Keras tutorial ▪ https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/ • CNN ▪ http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/ 1. Agenda¶ • Introduction to neural networks • Word/Document Vectors (vector representation of words/phrases/paragraphs) • Convolutionary neural network (CNN) • Application of CNN in text classification 2. Introduction neural
Deep Learning with keras¶ In this workshop, we will try to build some feedforward models to do sentiment analysis, using keras, a deep learning library: https://keras.io/ You will need pandas, keras (2.3.1) and tensorflow (2.1.0; and their dependencies) to run this code (pip install pandas keras==2.3.1 tensorflow-cpu==2.1.0). First let’s prepare the data. We are using
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7CCMFM18 Machine Learning¶ King’s College London Academic year 2019-2020 Lecturer: Blanka Horvath Example: Deep Hedging in the Black-Scholes Model¶ 2 March 2020 Let us first import the necessary libraries and functions, and set plotting style. In [1]: import numpy as np import numpy.random as npr from scipy.stats import norm import tensorflow.keras as keras import tensorflow.keras.backend as
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CS542 – Class Challenge – fine-grained classification of plants:¶ Our class challenge will consists of two tasks addressing an image recognition task where our dataset contains about 1K categories of plants with only about 250,000 images. There will be two parts to this task: 1. Image classification. Imagine we have cateloged all the plants we
COMP2420/COMP6420 – Introduction to Data Management, Analysis and Security Assignment – 2 Grading¶ Maximum Marks 100 Weight 20% of the Total Course Grade Submission deadline 7:00PM, Friday, May 24 Submission mode Electronic, Using GitLab One submission per group Estimated time 20 hours Penalty 100% after the deadline Submission¶ You need to submit the notebook Assignment-2.ipynb