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GDSC Machine Learning Session Presentation
- 1. Introduction to AI& Machine Learning
- 2. There are twomain approaches used in AI programming Rules-based Approach Machine Learning Approach
- 3. Rule Based Approach Rule-basedAI is a type of AI that uses rules to solve a problem. Human experts typically write rules but are not learned from data.
- 4. Machine Learning Approach MachineLearning gives computers the capability to learn from data without being explicitly programmed for it. It gives a computer ability to learn, just like a human.
- 6. Each approach hasits applications, advantages and disadvantages Next slides will show you some examples on where each approach is used
- 7. Rules-based approach Machine Learning Alphabetisinga list of song titles Rules-based Which Approach to Use?
- 8. Rules-based approach Machine Learning RankingWeb Search Results Rules-based Machine Learning Which Approach to Use?
- 9. Rules-based Machine Learning Predicting HousingPrices based on Location Machine Learning Which Approach to Use?
- 10. Processing online payments Rules-basedapproach Machine Learning Rules-based Which Approach to Use?
- 11. Classifying an objectin a photo Rules-based Machine Learning Machine Learning Which Approach to Use?
- 12. Each has itsbenefits Rule-based Approach Machine Learning – Rules are defined – Improvements come from algorithms and network – Learns patterns from data – Improvements may from additional data Recap
- 13. Collect Data Train and Test Model Define Objective Predictand Evaluate Focus on User Machine Learning Process
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- 17. MAMMAL WILDLIFE LION Classification In Classification, a programlearns from the given dataset or observations and then classifies new observation into a number of classes or groups.
- 18. Regression Regression is anML algorithm that is used to predict continuous outcomes after training on certain data.
- 19. Clustering is the taskof dividing the population or data points into a number of groups.
- 20. Sequence Prediction is predictingthe next symbol(s) based on the previously observed sequence of symbols
- 21. You can seeSequence Prediction in action by using Glide type, Handwriting recognition or Translation task on the Google’s Gboard app.
- 22. + = Style Transfer Styletransfer is a computer vision technique that takes two images—a content image and a style reference image—and blends them together so that the resulting output image retains the core elements of the content image, but appears to be “painted” in the style of the style reference image.
- 23. Applications of eachtechnique of Machine Learning
- 24. Classification Clustering Regression Sequence Prediction Style Transfer Classification Labelingemail as spam or not-spam Type of Machine Learning?
- 25. Classification Clustering Regression Sequence Prediction Style Transfer Identifyingtrends amongst a group of people who have bought a new music release Clustering Type of Machine Learning?
- 26. Classification Clustering Regression Sequence Prediction Style Transfer Typeof Machine Learning? Recommending next word in the android SMS app based on the words typed so far Sequence Prediction
- 27. Classification Clustering Regression Sequence Prediction Style Transfer Typeof Machine Learning? Determining workout activity based on phone movement. Classification
- 28. Classification Clustering Regression Sequence Prediction Style Transfer Typeof Machine Learning? A bot that reads the news in the voices of famous actors Style Transfer
- 29. Classification Clustering Regression Sequence Prediction Style Transfer Typeof Machine Learning? Identifying famous landmarks in a photo Classification
- 30. Classification Clustering Regression Sequence Prediction Style Transfer Typeof Machine Learning? Suggesting spelling corrections Clustering
- 31. Classification Clustering Regression Sequence Prediction Style Transfer Typeof Machine Learning? Predicting the quality score for an advertisement Regression
- 32. Classification Clustering Regression Sequence Prediction Style Transfer Typeof Machine Learning? Estimating arrival time based on time of day and traffic. Regression
- 33. Classification Clustering Regression Sequence Prediction Style Transfer Typeof Machine Learning? Translating between two languages Style Transfer
- 35. DL is machinelearning using neural networks. Glimpse of a basic neural network
- 36. Unlike former MLalgorithms, Neural Nets can handle image, video, audio and text.
- 37. TensorFlow is adeep learning framework developed by Google. TensorFlow makes it possible to build large deep neural networks without having to worry about all the mathematics behind it.
- 38. You can eitheruse existing models provided TensorFlow Or build your own neural network
- 39. TensorFlow can accelerateyour models using your GPU. This is made possible using Also, TensorFlow is highly scalable and can utilize the full power of multi GPU systems.
- 41. ⮚ This isone of most basic introductory example for TensorFlow. ⮚ We will use MNIST dataset included in TensorFlow by default. ⮚ We are going to train a small neural network for this task, just like you have seen before. Let’s try to recognize handwritten digits using TensorFlow Click to open Google Colab at start training the network 7 Input Output NN MNIST Dataset
- 42. Recent examples of DeepLearning
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- 44. Autonomous Vehicle • Depth Estimation •Pedestrian Detection • Road Sign Identification • Traffic Lights Detection • Road Lane Detection
- 45. Real Life Applications • Socialmedia apps use facial recognition to detect and tag users • Using computer vision, AR gear detect objects in real world in order to determine the locations on a device’s display to place a virtual object • Consumer devices use facial recognition to authenticate the identities of their owners • Law enforcement agencies also rely on facial recognition technology to identify criminals in video feeds
- 46. How It Works? DataLabelling Train and Test the Model Output And Improve Steps to Train the Model Convolutional Neural Network (MODEL)
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- 48. Advancements ⮚Few Shots Learning ⮚ImageCaptioning ⮚Super Resolution ⮚Action Recognition ⮚3D object Detection IMAGE CAPTIONING MODEL A Dog Sitting on the Beach 3D Object Detection
- 49. Getting Started • Basic: - OpenCV(you can use either python or C++) - Image Processing • Intermediate: - TensorFlow - Basic CNN Model for Classification(Cats vs Dogs)
- 50. Natural Language Processing Analysisand synthesis of natural language and speech. To make machines understand and draw out meaning and relationship between natural language
- 51. • Chatbots • LanguageTranslator • Sentiment Analysis • Email Classification and Filtering
- 52. Language Translator who areyou आप कौन हैं
- 53. •An organization mightuse sentiment analysis to classify reviews into different categories when the amount of reviews is large making it inefficient to manually classify them.
- 54. Recurrent Neural Networks Recurrent NeuralNetworks enable you to model time-dependent and sequential data problems, such as stock market prediction, machine translation, and text generation Why use RNN: When the sequence of the data is important and depicts a meaning then RNN is advantageous whereas machine learning algorithms like SVM fails to make use of and understand this sequencial meaning.
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- 56. More NLP Networks Longshort- term memory (LSTM) Gated Recurrent Unit Networks Bidirectional LSTM
- 57. Word2Vec It is usedto create a distributed representation of words into numerical vectors KING - MAN + WOMAN = QUEEN
- 58. ChatGPT Generative Pretrained Transformer GPT-3and GPT-4 are state-of-the-art language processing AI models developed by OpenAI They are capable of generating human-like text and have a wide range of applications, including language translation, language modelling, and generating text
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- 61. ⮚ You canknow more about TensorFlow at tensorflow.org ⮚ For beginners, Google has set-up a free crash course named Machine Learning Crash Course with TensorFlow APIs at https://developers.google.com/machine-learning/crash-course ⮚ offers a TensorFlow Developer Professional Certificate for those who want to excel at using this library. ⮚ And you can always go search on YouTube for free TensorFlow courses. Check out freecodecamp, they provides good free courses on YouTube. How to learn TensorFlow?
- 62. Free Courses Course Platform MachineLearning for Everybody – Full Course YouTube Machine Learning Specialization by Andrew Ng (Audited) Coursera Learn the Basics of Machine Learning Simplilearn
- 63. Paid Certificate Courses Course Platform Machine LearningSpecialization by Andrew Ng (with Certificate) Coursera Machine Learning with Python by IBM Coursera Deep Learning Specialization by Andrew Ng Coursera Machine Learning A-Z : AI, Python & R + ChatGPT Bonus [2023] Udemy
- 64. Kaggle is aextremely popular site among Machine Learning Enthusiasts and Data Scientists all over the world. It hosts many live ML and Data Science competitions each year. Many prominent programmers take part in these competitions. It has a huge repository of datasets which can be used by anyone for their projects.
- 65. Thank you! If anyof you have questions, please feel free to ask