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Integrating Artificial Intelligence with IoT
- 2. Agenda Intro to Machine Learning First Task Second Task Tensorflow Lite Intro to Neural Networks Intro to Convolutional Neural Networks
- 3. Intro to Machine Learning Learn, Adapt, Innovate: Your Guide to Machine Learning Let¡¯s start ?
- 4. How Humans learn ?
- 5. How Humans learn ? Instruction Experience and Practice Feedback Think of this as a teacher providing clear examples Similar to discovering patterns or groups in new information without explicit guidance Receiving feedback from the environment or others about the consequences of actions, which helps in adjusting behaviors.
- 6. How Computer Learn ?
- 7. The simple definition of "Artificially intelligent" is attempting to simulate human intelligence, including aspects that involve learning, which we aim to replicate through Machine Learning techniques.
- 8. Thank You Types Of Machine Learning
- 10. UnSupervised Learning Unsupervised learning is a type of machine learning technique in which an algorithm discovers patterns and relationships using unlabeled data. The primary goal of Unsupervised learning is often to discover hidden patterns, similarities, or clusters within the data which can help in data exploration, visualization, dimensionality reduction, and more
- 11. Clustering The process of grouping data points into clusters based on their similarity. This technique is useful for identifying patterns and relationships in data without the need for labeled examples Algorithms: K-means, Mean-shift..etc. One main category of UnSupervised Learning
- 12. Supervised Learning Supervised learning is defined as when a model gets trained on a ¡°Labelled Dataset¡±. Labelled datasets have both input (X) and output parameters (Y). In Supervised Learning, algorithms learn to map points between inputs and correct outputs. Supervised Learning models have a baseline understanding of what the correct output values should be
- 13. Two main categories of Supervised Learning Classification Regression Deals with predicting categorical target variables, which represent discrete classes or labels. For instance, classifying emails as spam or not spam, or predicting whether a patient has a high risk of heart disease. Algorithms: Logistic Regression, SVM, Decision Tree, KNN..etc Deals with predicting continuous target variables, which represent numerical values. For example, predicting the price of a house based on its size and location, or forecasting the sales of a product. Algorithms: Linear Regression, Ridge Regression, Lasso Regression..etc
- 14. In Conclusion, The key difference between supervised and unsupervised Learning is the Type of data used, whether it¡¯s Labelled or not Here, the data is labeled, so what we did is draw the line to separate the classes from one another On the other hand, the data isn¡¯t labeled so we¡¯re grouping similar points together, based on its attributes/features
- 15. 450$ 150$ 250$ 100 m 200 m 2 2 400 m 2 House Pricing Problem
- 16. 300 m 2
- 17. Y = W X + b 150 100 250 200 400 450 = = = + + + ? ?
- 18. 100 m? 200 m? 300 m? 400 m? 0 100 200 300 400 500 Linear Regression
- 19. W b Parameters
- 20. W We ght 100 m 200 m 2 2 400 m 2 300 m 2
- 21. b Bias
- 22. Model is trying to get the best parameters... What is Training ?
- 23. Y = W X + b 150 100 250 200 400 450 = = = + + + 0.5 x 0.5 x 0.5 x 100 100 100
- 24. Y = W X + b 150 100 250 200 400 450 = = = + + + 1 x 1 x 1 x 50 50 50
- 25. Solving these equations, We get Model adjusted its parameters M=100, C=50
- 26. Y = W X + B ? = 1 x 300 + 50 = 350 a 300 m house would cost you 350$ 2
- 27. Our Model has finished training successfully
- 28. What if we have Multiple Features ?
- 29. W We ght 100 m 200 m 2 2 400 m 2 300 m 2 1
- 30. W Weight 2
- 31. 100 m 200 m 2 2 400 m 2 300 m 2 Y = W X + W X + b 1 2 1 2 Multiple Linear Regression
- 32. What is gradient descent? Gradient Descent is an optimization algorithm used in machine learning to minimize the cost function (or error) by iteratively adjusting the model's parameters It works by calculating the gradient (slope) of the cost function with respect to the parameters and then moving the parameters in the opposite direction of the gradient to reduce the error. The process continues until the model reaches the lowest possible error.
- 33. it's like walking down a hill (the cost function) to find the lowest point (the optimal solution) by taking small steps in the direction that decreases the slope the most. In Simple Terms
- 34. Recap
- 35. Instruction For instance, a student learns math by solving problems with provided solutions to check correctness. Machine Learning Model is trained on a dataset that includes input-output pairs, where the correct output for each input is provided. This process is similar to how a student learns math problems with provided solutions
- 36. student exploring different types of plants and identifying common characteristics without prior knowledge. Machine Learning Model produces clusters or groupings of plants that share similar characteristics. Each cluster represents a group of plants with common features. Experience & Practice
- 37. ?A student starts at a new school and needs to find their way to various locations like classrooms, the cafeteria. Machine Learning Model learns to navigate the school (environment) by exploring different routes (actions) and receiving feedback (rewards) based on their performance. Feedback
- 38. Machine learning & Internet of Things Predictive Maintenance Use Case: Predicting equipment failures before they occur based on sensor data (e.g., temperature, vibration). Smart Healthcare Use Case: Remote patient monitoring and early detection of health issues. Smart Cities Use Case: Optimizing traffic flow based on real-time data from sensors. Etc...
- 39. Thank You Types Of AI Learning Algorithms
- 40. Types Of AI Learning Algorithms Machine Learning (ML) Deep Learning (DL) Natural Language Processing (NLP) Supervised Learning (Classification - Regression) Unsupervised Learning (Clustering) Reinforcement Learning (RL) Neural Network (NNs) Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Long Short-Term Memory (LSTMs) Bidirectional LSTM (BiLSTM) Transformers (Encoders - Decoders) (BERTs - GPTs ) Generative Adversarial Networks (GANs) Diffusion Models AI ML DL NN
- 41. First Kahoot
- 42. Task 1: Developing a Machine Learning Model Using LDR Readings In this task, we aim to develop a machine learning model that utilizes pre- collected Infrared sensor (IR) readings to predict the distance in cm. The steps involved include: Data Preprocessing: 1. Utilize the provided CSV file containing IR analog signal data (ranging from 0 to 4095) and preprocess it for training the model. Model Training: 2. Design and train a simple neural network using a Jupyter Notebook (.ipynb file). The model will learn the relationship between the input readings and the corresponding lighting conditions. Deployment on ESP32: 3. After training, the model will be converted and deployed onto an ESP32 microcontroller. The ESP32 will use the model to take real-time IR readings and transform it into distance in cm. Let¡¯s start ?
- 43. Intro to Neural Networks From Data to Decisions: Understanding Neural Networks Let¡¯s start ?
- 44. Single Layer Neural Network Architecture Input Layer Hidden Layer Output Layer Neuron Weight Y Z
- 45. Neural Network Architecture Hidden Layer Y Z Y = W X + b Z = F ( x ) Activation Functions
- 46. Hidden Layer Y Z Z = F ( x ) Why do we need Activation Functions ? Activation Functions They introduce non-linearity into the model, allowing it to learn and represent complex patterns. Without activation functions, the network would only be able to learn linear relationships.
- 47. Hidden Layer Y Z Z = F ( x ) Activation Functions
- 48. Activation Functions Mainly used for Binary Classification 1 0
- 49. Neural Network Architecture Input Layer Hidden Layer Output Layer x1 x2
- 50. Why Not Linear Activation Function? Input Layer Hidden Layer Output Layer x1 x2 Result of Activation Function The Actual Value of a Perceptron
- 52. Deployment ?
- 53. Intro to CNN Convolutional Neural Networks Where Vision Meets Intelligence Let¡¯s start ?
- 54. ?The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples. . MNIST Dataset
- 55. The kernel is a filter that is used to extract the features from the images. The kernel is a matrix that moves over the input data, performs the dot product with the sub- region of input data, and gets the output as the matrix of dot products. What is a kernel ? 3x3
- 56. What Kernels do ? x
- 57. ºÝºÝߣ the Kernel over the image to Extract Features
- 58. Main Component in the CNN Architecture is Conv Layer!! Conv Layer is a set of kernels!!
- 59. CNN Architecture
- 60. So... What is the difference between Neural Networks (NN) and Convolution Neural Networks (CNN)?
- 61. 1 2 3 4 28 . . . 1 2 3 4 28 . . . 28x28
- 62. How can we take 2D input and insert it as input for NN ? Let¡¯s squeeze them into 1D Array !!
- 63. The image, instead of appearing as a 3D matrix, now appears as a single row of numbers representing the pixel values.
- 64. Is that Efficient? True & False at the same time
- 65. 0 -1 0 -1 5 -1 0 -1 0 Because we lose the relationships between surrounding pixels.
- 66. What about in CNN ? We don¡¯t, that¡¯s why the accuracy shoots up!
- 68. Previously...
- 70. TensorFlow Lite (TFLite) is a collection of tools to convert and optimize TensorFlow models to run on mobile and edge devices. You can find ready-to-run TensorFlow Lite models for a wide range of ML/AI tasks, or convert and run TensorFlow and PyTorch models to the TFLite format using the AI Edge conversion and optimization tools.
- 71. Key Features Optimized for on-device machine learning Multi-platform support Multi-framework model options Diverse language support High performance
- 72. Second Kahoot
- 73. Project
- 74. Project Overview Simple Classification Model predicts the state of a dependent variable ( ? ) Rain based on independent variable. ( x ) Humidity& Temperature. Get Started Step 1: Data Collection Step 2: Train a Simple Model Step 3: Export Our Model for tflite Step 4: Convert Model to C++ Step 5: Add TensorFlow Lite Library Step 6: Add TensorFlow Lite Model Step 7: Build & Upload Code
- 75. Resources Machine Learning Specialization 1. https://www.coursera.org/specializations/machine-learning-introduction 1.2. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow 2. Deep Learning Specialization https://www.coursera.org/specializations/deeplearning 3. Natural Language Processing https://www.coursera.org/specializations/natural-language-processing 4. Generative Adversarial Networks GANs https://www.coursera.org/specializations/generative-adversarial-networks-gans
- 77. Supervised Unsupervised Classification Clustering Other types of Regression Gradient Descent Feed Forward Backpropagation