Introduction to Machine Learning
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This document provides an overview of machine learning concepts including the different types of machine learning problems like regression, classification, clustering, and recommender systems. It also discusses various machine learning algorithms like linear regression, logistic regression, and deep learning techniques. Finally, it covers tools used for machine learning like Python, NumPy, Pandas, Scikit-learn and TensorFlow as well as next steps for learning machine learning through online courses and competitions.
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발표자: 장원동 (고려대 박사과정)
발표일: 2017.8.
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A semi-supervised online video object segmentation algorithm, which accepts user annotations about a target object at the first frame, will be presented. It propagates the segmentation labels at the previous frame to the current frame using optical flow vectors.
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This document discusses techniques for data cleansing, handling missing data, calculating important statistical functions, performing Spearman correlation ranking, and using Principal Component Analysis. It describes methods for cleaning data by checking for validity, accuracy, completeness, consistency and uniformity. It also outlines approaches for handling missing data through various replacement and imputation techniques. Finally, it defines statistical functions like mean, median, mode, standard deviation, skewness, kurtosis and percentile and explains how Principal Component Analysis works to project data onto principal components ordered by covered variance.
Leapfrog in deep learning
This fast paced hands on worskhop is designed to bootstrap your Deep Learning. It quickly on boards Machine Learning concepts like regression, classification, matrix factorization etc. It introduces algorithms like k Nearest Neighbors, k means, recommender systems etc. It brings in tools like python for quick coding, pandas and numpy for data munging, matplotlib for visualization, scikit-learn for ready made machine learning algorithms. It does so with real life use cases like predicting house sale prices, sentiment analysis using restaurant reviews; real life data like people wikipedia, adult income data etc. and lots of hands on coding. We look at deep learning, deep features and transfer learning in the context of image recognition. We dive into intuition behind commonly popular algorithm of gradient descent, forward and backward propagation in neural networks. This approach helps imbibe the concepts effectively. We go onto implementing logistic regression as single layer neural network from scratch completely in python. Later on we implement generic multi layer neural network in tensorflow.
You can download the entire course content (follow along slides, data for hands on assignments, developed code for all hands on assignments) from github repository of https://github.com/sameermahajan/MLWorkshop During the course you will develop all the code outlined here from scratch under the guidance of the instructor. I hope that you continue referring to programs developed here for tools, technologies and techniques (3 Ts) even as you progress through your Deep Learning career! Good Luck!
You can develop and execute all the code with ready made tensorflow docker image from Google Container Registry. You need docker installed on your machine. To run the container:
sudo docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow
It also runs jupyter server that you can connect to from your browser using the URL:
http://localhost:8888/?token=(token id)
to start your notebook to begin coding.
(Note that above jupyter connectivity would also work across machines as long as there is network connectivity between them).
You can get the container id of the running docker container id by running:
sudo docker ps
You can enter the container (for say installing some custom package like scikit-surprise used by one of the programs on recommenders here) by running:
sudo docker exec -it (container id) bash
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Introduction to Machine Learning
- 3. ML Paradigm Shift Insight Intelligence Model f input Traditional Programming output ML input / data / features /x output / labels / y f
- 5. Types of problems and solutions • Regression: real valued output, predicting house prices • Classification: product reviews • Clustering: unsupervised learning, document retrieval • Recommender systems: product recommendation • Deep learning: neural networks • Time series: forecasting • NLP / NLU: GPT3 • Image Recognition – Computer Vision • Acoustics
- 6. Regression • Predicting house prices • Linear regression • Logistic regression • Multi variate • Polynomial Regression • Ridge • Lasso
- 7. y = f(x) = w0 + w1 * x Linear regression
- 8. Cost / Loss Function w0 w1 RSS(w0, w1)
- 9. Gradient Descent x y x0, y0 x1, y1 step x2, y2 xmin, ymin … yn = yn – 1 – alpha * d yn – 1 /d xn – 1 gradient gradient = 0 alpha = learning rate = xn - xn – 1 Gradient / slope at point (xn,yn) = d yn /d xn
- 11. Tools & Technologies • Jupyter notebook • Python • Numpy • Pandas • Matplotlib • Scikit learn, Tensorflow, Pytorch
- 12. Solving Linear Regression import pandas as pd sales = pd.read_csv('seattle_house_sales.csv') from sklearn import linear_model regr = linear_model.LinearRegression() sqft_model = regr.fit(train.sqft_living.values.reshape(-1, 1), train.price.values.reshape(-1, 1)) import matplotlib.pyplot as plt plt.scatter(train['sqft_living'], train['price'], color='black') plt.plot(train['sqft_living'], sqft_model.predict(train['sqft_living']. values.reshape(-1, 1)), color='blue', linewidth=3) plt.xticks(()) plt.yticks(()) plt.show()
- 13. Classification • Sentiment analysis • Analyze restaurant reviews
- 16. Clustering • Unsupervised learning • Document retrieval
- 17. #occurrences of a word in a document
- 18. Document Vectors word1 word2 word3 …… wordn Document1 ( N11 N12 N13 ….. N1n) Document2 ( N21 N22 N23 ….. N2n) . . . Dcoumentm ( Nm1 Nm2 Nm3 ….. Nmn)
- 20. Recommenders • Netflix movie recommendations based on user ratings • Song recommender based on user listen count • Facebook friend recommender • Popularity based: not personalized • Classification based: features may not be available • Co-occurrence based: who bought this also bought…
- 26. Time series - fbprophet • Semi supervised • Time as feature • Data as y • Components • Trend : upward / downward • Seasonality : day of the week • Cycle : every 5 years • Noise • Usually a combination of above components • Forecasting
- 27. Future • Acoustics - Speech recognition • Video processing • Robotics • Alpha Go Zero • Self driving cars
- 28. Challenges • Model selection • Feature engineering • Scaling • Data • Model • Special architectures • Parallel processing • GPUs
- 29. Next steps – Online courses • https://github.com/sameermahajan/MLWorkshop • Coursera • Machine learning specialization • Machine learning by Andrew Ng, Stanford • Deep learning specialization • Udemy • Machine Learning A to Z • Deep Learning A to Z • Udacity • Machine Learning Engineer • Deep Learning Foundation Nanodegree Program
- 30. Next steps - contd • Online competitions • kaggle • Online datasets to play with • https://www.kaggle.com/datasets • http://mldata.org/repository/data/ • http://archive.ics.uci.edu/ml/index.php • http://deeplearning.net/datasets/ • https://deeplearning4j.org/opendata • https://catalog.data.gov/dataset • Formulate your own problem, gather data, model, evaluate and keep refining it further
- 31. Q&A