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ML Starter Pack A comprehensive guide to Machine Learning April 22, 2023 GDG Waterloo, Canada

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I'm Charmi! ● ML Grad at Mila and UdeM ● GDE for ML ● International Speaker ● Worked @ AWS, Shipmnts, ISRO ● Tech, Art, Travel

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Plan of the Day! ● What is Machine Learning? ● Types of ML ● Supervised and Unsupervised techniques ● Classification and Regression ● Basics of Deep Learning ● Basics of NLP, CV, ASR ● The 3D ML Pipeline ● Fun Applications

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? 90% have failed to solve this... = = = 33 30 36

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99% have failed to solve this...

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Retrospection time!! What we just did? We solved logical math puzzle in a fraction of time

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Think about, How we solved them? We had inputs We also had outputs

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Think about, How we solved them? We created Rules and Patterns in our brain!!

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Can machines create rules on their own?

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Can machines create rules on their own? No, if it’s Classical Programming...

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Can machines create rules on their own? YES, if it’s Machine Learning!!!

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Classical Programming vs ML Classical Programming Machine Learning Rules Rules Data Data Answers Answers

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Artificial Intelligence Deep Learning Machine Learning Any Technique that enables computers to mimic human intelligence & behaviour A subset of ML exposing multilayered neural networks to vast amount of data A subset of AI that includes statistical techniques to solve the tasks using experience

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Basic Terminologies ● Features ● Labels ● Examples ○ Labelled example ○ Unlabelled example ● Data Split (Train, Valid, Test) ● Models (Train and Test) ○ Classification model ○ Regression model

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Supervised Learning ● Supervised Learning deals with prediction of values based on given combinations of data and labels given beforehand ● ML systems learn how to combine input to produce useful predictions on never-before-seen data ● It is like learning with a teacher

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Regression and Classification ● A regression model predicts continuous values by fitting a line ○ What is the value of a house in California? ○ What is the probability that a user will click on this ad? ● A classification model predicts discrete values by creating boundaries ○ Is a given email message spam or not spam? ○ Is this an image of a dog, a cat, or a hamster?

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Iterative Learning

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Overfitting vs Underfitting ● An overfit model gets a low loss during training but does a poor job predicting new data ● Overfitting is caused by making a model more complex than necessary ● The fundamental tension of machine learning is between fitting our data well, but also fitting the data as simply as possible

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Unsupervised Learning ● It deals with clustering values or forming groups of values ● One aims to infer patterns from the data rather than predicting values ● It is like learning on your own

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Evaluation Metrics https://en.wikipedia.org/wiki/Precision_and_recall, https://www.kdnuggets.com/2018/06/right-metric-evaluating-machine-learning-models-2.html

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Evaluation Metrics https://en.wikipedia.org/wiki/Precision_and_recall, https://www.kdnuggets.com/2018/06/right-metric-evaluating-machine-learning-models-2.html

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Deep Learning

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Neural Network https://gfycat.com/gifs/search/neural+network

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When to use or not use DL? ● Deep Learning outperforms other techniques if the data size is large. But with small data size, traditional Machine Learning algorithms are preferable ● Finding large amount of “Good” data is always a painful task ● Deep Learning techniques need to have high end infrastructure to train in reasonable time ● When there is lack of domain understanding for feature introspection, Deep Learning techniques outshines others as you have to worry less about feature engineering

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When to use or not use DL? ● Model Training time: a Deep Learning algorithm may take weeks or months whereas, traditional Machine Learning algorithms take few seconds or hours ● DL never reveals the “how and why” behind the output- it’s a Black Box ● Deep Learning really shines when it comes to complex problems such as image classification, natural language processing, and speech recognition ● DL excels in tasks where the basic unit (pixel, word) has very little meaning in itself, but the combination of such units has a useful meaning

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Natural Language Processing ● Sentiment analysis ● Chatbots ● Machine translation ● Speech recognition ● Text summarization

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Computer Vision ● Facial recognition ● Object detection ● Medical imaging ● Autonomous vehicles ● Segmentation

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Speech Understanding ● Voice-controlled personal assistants ● Virtual agents ● Dictation software ● Closed captioning ● Subtitling for movies ● Transcribing

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The 3D ML Pipeline Design - Develop - Deploy

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Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning

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Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning

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Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning

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Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning

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Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning

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Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning

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Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning

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Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning

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Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning

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Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning

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Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning

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It’s time for exploring some FUN applications

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Classification on Custom Data https://teachablemachine.withgoogle.com/train

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Sketch-RNN https://magenta.tensorflow.org/assets/sketch_rnn_demo/index.html

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From where the data came Quick Draw: https://quickdraw.withgoogle.com/

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RUNN = 🏃Run + 🤖RNN http://vibertthio.com/runn/

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Experiments with Google https://experiments.withgoogle.com/collection/ai

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The project Magenta https://magenta.tensorflow.org/

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https://developers.google.com/machine-learning/

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Let's connect! @CharmiChokshi Charmi Chokshi @charmichokshi coda.io/@charmi-chokshi/hi

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Thanks! Questions? Download the deck