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The Google Developer Student's Club IIT BHU is in collaboration with the Astronomy Club IIT BHU to present an out-of-this-world opportunity! Introducing our joint venture: the ML in Astronomy Workshop Series! Get ready to explore the captivating synergy between Machine Learning and Astronomy. This isn't your ordinary lecture; it's a hands-on cosmic adventure. You'll be diving into ML applications in Astronomy and flexing your coding skills, so be sure to bring your trusty laptop. No Prerequisites Needed: Your enthusiasm and curiosity are your passports to this cosmic journey.

- 1. An Introduction to Machine Learning and Deep Learning Workshop-01
- 2. What is Machine Learning?
- 4. Linear Regression Linear Regression is the supervised Machine Learning model in which the model finds the best fit linear line between the independent and dependent variable i.e. it finds the linear relationship between the dependent and independent variable.
- 5. Parameters : Hypothesis: Cost Function: Goal: • Our Goal now is to minimise the error. i.e. to minimise the Cost function. • We need to find the perfect parameters such that the mean error is minimum. Understanding the Mathematical and Intuitive Aspects
- 6. Logistic Regression • Type of statistical model (also known as logit model), often used for classification and predictive analytics. • Logistic regression estimates the probability of an event occurring, such as voting or not voting, based on a given dataset of independent variables.
- 7. we have, We use the "Sigmoid Function," also called the "Logistic Function": g(z) outputs a value between 0 and 1 Cost Function: Prediction :
- 8. Remember that the general form of gradient descent is: We can fully write out our entire cost function as follows: Gradient Descent:
- 9. k-nearest neighbours algorithm This algorithm is based on the assumption that data points that are close to each other in space are more likely to belong to the same class. Choosing the value of K:
- 10. What is Unsupervised learning? • •
- 11. k-means algorithm Step 0: Randomly initialise k cluster centroids. Repeat { Step 1: Assign points to cluster centroids Step 2: Move cluster centroids. }
- 15. The people in these photos are infact not real. Yes!! These people do not exist. reference: thispersondoesnotexist.com An architecture called StyleGAN is used to generate these almost real faces. StyleGAN is a modified architecture of Generative Adversarial Networks(GANs) which is capable of generating real-life images
- 16. Artificial Neural Networks The term "Artificial Neural Network" is derived from Biological neural networks that develop the structure of a human brain. Similar to the human brain that has neurons interconnected to one another, artificial neural networks also have neurons that are interconnected to one another in various layers of the networks. These neurons are known as nodes. A Biological Neuron A typical ANN
- 17. Mathematics behind Neural Networks
- 18. Convolutional Neural Networks • A Convolutional Neural Network, also known as CNN or ConvNet, is a class of neural networks that specializes in processing data that has a grid-like topology, such as an image. • A digital image is a binary representation of visual data. It contains a series of pixels arranged in a grid-like fashion that contains pixel values to denote how bright and what color each pixel should be. A CNN typically has three layers: convolutional,pooling and a fully connected layer.
- 19. Principle of convolution • The principle of the convolution is to slide across the input image from the left to the right and from the top to the bottom using a specific size window. • The sliding window in the CNN is called the filter (or kernel), and the area slipped by the filter is called the receptive field. • The matrix and the pixel values of the images multiply when the convolutional layer passes the filter after that the values are added and then deviation value is added. 𝑦=∑(𝑥𝑖𝑗×𝑓𝑖𝑗)+𝑏 Calculation process for the features of the convolution layer
- 20. Recurrent Neural Networks • A recurrent neural network (RNN) is a type of artificial neural network which uses sequential data or time series data. • These deep learning algorithms are commonly used for ordinal or temporal problems, such as language translation, natural language processing (nlp), speech recognition, and image captioning. • Like feedforward and convolutional neural networks (CNNs), recurrent neural networks utilize training data to learn. They are distinguished by their “memory” as they take information from prior inputs to influence the current input and output. • While traditional deep neural networks assume that inputs and outputs are independent of each other, the output of recurrent neural networks depend on the prior elements within the sequence.
- 21. Transformers
- 22. Generative Adversarial Networks(GANs) • Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks. • Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset. • GANs are made up of two neural networks named Generator and Discriminator. • The generator part of a GAN learns to create fake data by incorporating feedback from the discriminator. It learns to make the discriminator classify its output as real. • The discriminator in a GAN is simply a classifier. It tries to distinguish real data from the data created by the generator. It could use any network architecture appropriate to the type of data it's classifying.
- 23. Resource s • Andrew NG machine learning specialisatiation • Pytorch Turtorials by Daniel Bourke • TensorFlow tutorials by Alladin perssson • Andrew NG Deep Learning Specialisation • Summer Analytics 2023, IIT Guwahati