| Name | Download | Download Size |
|---|---|---|
| Lecture Note | Download as zip file | 106M |
| Module Name | Download |
|---|---|
| noc19_cs82_assignment_Week_1 | noc19_cs82_assignment_Week_1 |
| noc19_cs82_assignment_Week_10 | noc19_cs82_assignment_Week_10 |
| noc19_cs82_assignment_Week_11 | noc19_cs82_assignment_Week_11 |
| noc19_cs82_assignment_Week_12 | noc19_cs82_assignment_Week_12 |
| noc19_cs82_assignment_Week_2 | noc19_cs82_assignment_Week_2 |
| noc19_cs82_assignment_Week_3 | noc19_cs82_assignment_Week_3 |
| noc19_cs82_assignment_Week_4 | noc19_cs82_assignment_Week_4 |
| noc19_cs82_assignment_Week_5 | noc19_cs82_assignment_Week_5 |
| noc19_cs82_assignment_Week_6 | noc19_cs82_assignment_Week_6 |
| noc19_cs82_assignment_Week_7 | noc19_cs82_assignment_Week_7 |
| noc19_cs82_assignment_Week_8 | noc19_cs82_assignment_Week_8 |
| noc19_cs82_assignment_Week_9 | noc19_cs82_assignment_Week_9 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Introduction to the Course History of Artificial Intelligence | Download |
| 2 | Overview of Machine Learning | Download |
| 3 | Why Linear Algebra ? Scalars, Vectors, Tensors | Download |
| 4 | Basic Operations | Download |
| 5 | Norms | Download |
| 6 | Linear Combinations Span Linear Independence | Download |
| 7 | Matrix Operations Special Matrices Matrix Decompositions | Download |
| 8 | Introduction to Probability Theory Discrete and Continuous Random Variables | Download |
| 9 | Conditional, Joint, Marginal Probabilities Sum Rule and Product Rule Bayes' Theorem | Download |
| 10 | Bayes' Theorem - Simple Examples | Download |
| 11 | Independence Conditional Independence Chain Rule Of Probability | Download |
| 12 | Expectation | Download |
| 13 | Variance Covariance | Download |
| 14 | Some Relations for Expectation and Covariance (Slightly Advanced) | Download |
| 15 | Machine Representation of Numbers, Overflow, Underflow, Condition Number | Download |
| 16 | Derivatives,Gradient,Hessian,Jacobian,Taylor Series | Download |
| 17 | Matrix Calculus (Slightly Advanced) | Download |
| 18 | Optimization – 1 Unconstrained Optimization | Download |
| 19 | Introduction to Constrained Optimization | Download |
| 20 | Introduction to Numerical Optimization Gradient Descent - 1 | Download |
| 21 | Gradient Descent – 2 Proof of Steepest Descent Numerical Gradient Calculation Stopping Criteria | Download |
| 22 | Introduction to Packages | Download |
| 23 | The Learning Paradigm | Download |
| 24 | A Linear Regression Example | Download |
| 25 | Linear Regression Least Squares Gradient Descent | Download |
| 26 | Coding Linear Regression | Download |
| 27 | Generalized Function for Linear Regression | Download |
| 28 | Goodness of Fit | Download |
| 29 | Bias-Variance Trade Off | Download |
| 30 | Gradient Descent Algorithms | Download |
| 31 | Introduction to Week 5 (Deep Learning) | Download |
| 32 | Logistic Regression | Download |
| 33 | Binary Entropy cost function | Download |
| 34 | OR Gate Via Classification | Download |
| 35 | NOR, AND, NAND Gates | Download |
| 36 | XOR Gate | Download |
| 37 | Differentiating the sigmoid | Download |
| 38 | Gradient of logistic regression | Download |
| 39 | Code for Logistic Regression | Download |
| 40 | Multinomial Classification- Introduction | Download |
| 41 | Multinomial Classification - One Hot Vector | Download |
| 42 | Multinomial Classification - Softmax | Download |
| 43 | Schematic of multinomial logistic regression | Download |
| 44 | Biological neuron | Download |
| 45 | Structure of an Artificial Neuron | Download |
| 46 | Feedforward Neural Network | Download |
| 47 | Introduction to back prop | Download |
| 48 | Summary of Week 05 | Download |
| 49 | Introduction to Convolution Neural Networks (CNN) | Download |
| 50 | Types of convolution | Download |
| 51 | CNN Architecture Part 1 (LeNet and Alex Net) | Download |
| 52 | CNN Architecture Part 2 (VGG Net) | Download |
| 53 | CNN Architecture Part 3 (GoogleNet) | Download |
| 54 | CNN Architecture Part 4 (ResNet) | Download |
| 55 | CNN Architecture Part 5 (DenseNet) | Download |
| 56 | Train Network for Image Classification | Download |
| 57 | Semantic Segmentation | Download |
| 58 | Hyperparameter optimization | Download |
| 59 | Transfer Learning | Download |
| 60 | Segmentation of Brain Tumors from MRI using Deep Learning | Download |
| 61 | Activation Functions | Download |
| 62 | Learning Rate decay, Weight initialization | Download |
| 63 | Data Normalization | Download |
| 64 | Batch Norm | Download |
| 65 | Introduction to RNNs | Download |
| 66 | Example - Sequence Classification | Download |
| 67 | Training RNNs - Loss and BPTT | Download |
| 68 | Vanishing Gradients and TBPTT | Download |
| 69 | RNN Architectures | Download |
| 70 | LSTM | Download |
| 71 | Why LSTM Works | Download |
| 72 | Deep RNNs and Bi- RNNs | Download |
| 73 | Summary of RNNs | Download |
| 74 | Introduction. | Download |
| 75 | Knn | Download |
| 76 | Binary decision trees | Download |
| 77 | Binary regression trees | Download |
| 78 | Bagging | Download |
| 79 | Random Forest | Download |
| 80 | Boosting | Download |
| 81 | Gradient boosting | Download |
| 82 | Unsupervised learning & Kmeans | Download |
| 83 | Agglomerative clustering | Download |
| 84 | Probability Distributions- Gaussian, Bernoulli | Download |
| 85 | Covariance Matrix of Gaussian Distribution | Download |
| 86 | Central Limit Theorem | Download |
| 87 | Naïve Bayes | Download |
| 88 | MLE Intro | Download |
| 89 | PCA-part 1 | Download |
| 90 | PCA-part 2 | Download |
| 91 | Support Vector Machines | Download |
| 92 | MLE, MAP and Bayesian Regression | Download |
| 93 | Introduction to Generative model | Download |
| 94 | Generative Adversarial Networks (GAN) | Download |
| 95 | Variational Auto-encoders (VAE) | Download |
| 96 | Applications: Cardiac MRI - Segmentation & Diagnosis | Download |
| 97 | Applications: Cardiac MRI Analysis - Tensorflow code walkthrough | Download |
| 98 | Introduction to Week 12 | Download |
| 99 | Application 1 description - Fin Heat Transfer | Download |
| 100 | Application 1 solution | Download |
| 101 | Application 2 description - Computational Fluid Dynamics | Download |
| 102 | Application 2 solution | Download |
| 103 | Application 3 description - Topology Optimization | Download |
| 104 | Application 3 solution | Download |
| 105 | Application 4 - Solution of PDE/ODE using Neural Networks | Download |
| 106 | Summary and road ahead | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Introduction to the Course History of Artificial Intelligence | Download Verified |
| 2 | Overview of Machine Learning | Download Verified |
| 3 | Why Linear Algebra ? Scalars, Vectors, Tensors | Download Verified |
| 4 | Basic Operations | Download Verified |
| 5 | Norms | Download Verified |
| 6 | Linear Combinations Span Linear Independence | Download Verified |
| 7 | Matrix Operations Special Matrices Matrix Decompositions | Download Verified |
| 8 | Introduction to Probability Theory Discrete and Continuous Random Variables | Download Verified |
| 9 | Conditional, Joint, Marginal Probabilities Sum Rule and Product Rule Bayes' Theorem | Download Verified |
| 10 | Bayes' Theorem - Simple Examples | Download Verified |
| 11 | Independence Conditional Independence Chain Rule Of Probability | Download Verified |
| 12 | Expectation | Download Verified |
| 13 | Variance Covariance | Download Verified |
| 14 | Some Relations for Expectation and Covariance (Slightly Advanced) | Download Verified |
| 15 | Machine Representation of Numbers, Overflow, Underflow, Condition Number | Download Verified |
| 16 | Derivatives,Gradient,Hessian,Jacobian,Taylor Series | Download Verified |
| 17 | Matrix Calculus (Slightly Advanced) | Download Verified |
| 18 | Optimization – 1 Unconstrained Optimization | Download Verified |
| 19 | Introduction to Constrained Optimization | Download Verified |
| 20 | Introduction to Numerical Optimization Gradient Descent - 1 | Download Verified |
| 21 | Gradient Descent – 2 Proof of Steepest Descent Numerical Gradient Calculation Stopping Criteria | Download Verified |
| 22 | Introduction to Packages | Download Verified |
| 23 | The Learning Paradigm | Download Verified |
| 24 | A Linear Regression Example | Download Verified |
| 25 | Linear Regression Least Squares Gradient Descent | Download Verified |
| 26 | Coding Linear Regression | Download Verified |
| 27 | Generalized Function for Linear Regression | Download Verified |
| 28 | Goodness of Fit | Download Verified |
| 29 | Bias-Variance Trade Off | Download Verified |
| 30 | Gradient Descent Algorithms | Download Verified |
| 31 | Introduction to Week 5 (Deep Learning) | Download Verified |
| 32 | Logistic Regression | Download Verified |
| 33 | Binary Entropy cost function | Download Verified |
| 34 | OR Gate Via Classification | Download Verified |
| 35 | NOR, AND, NAND Gates | Download Verified |
| 36 | XOR Gate | Download Verified |
| 37 | Differentiating the sigmoid | Download Verified |
| 38 | Gradient of logistic regression | Download Verified |
| 39 | Code for Logistic Regression | Download Verified |
| 40 | Multinomial Classification- Introduction | Download Verified |
| 41 | Multinomial Classification - One Hot Vector | Download Verified |
| 42 | Multinomial Classification - Softmax | Download Verified |
| 43 | Schematic of multinomial logistic regression | Download Verified |
| 44 | Biological neuron | Download Verified |
| 45 | Structure of an Artificial Neuron | Download Verified |
| 46 | Feedforward Neural Network | Download Verified |
| 47 | Introduction to back prop | Download Verified |
| 48 | Summary of Week 05 | Download Verified |
| 49 | Introduction to Convolution Neural Networks (CNN) | Download Verified |
| 50 | Types of convolution | Download Verified |
| 51 | CNN Architecture Part 1 (LeNet and Alex Net) | Download Verified |
| 52 | CNN Architecture Part 2 (VGG Net) | Download Verified |
| 53 | CNN Architecture Part 3 (GoogleNet) | Download Verified |
| 54 | CNN Architecture Part 4 (ResNet) | Download Verified |
| 55 | CNN Architecture Part 5 (DenseNet) | Download Verified |
| 56 | Train Network for Image Classification | Download Verified |
| 57 | Semantic Segmentation | Download Verified |
| 58 | Hyperparameter optimization | Download Verified |
| 59 | Transfer Learning | Download Verified |
| 60 | Segmentation of Brain Tumors from MRI using Deep Learning | Download Verified |
| 61 | Activation Functions | Download Verified |
| 62 | Learning Rate decay, Weight initialization | Download Verified |
| 63 | Data Normalization | Download Verified |
| 64 | Batch Norm | Download Verified |
| 65 | Introduction to RNNs | Download Verified |
| 66 | Example - Sequence Classification | Download Verified |
| 67 | Training RNNs - Loss and BPTT | Download Verified |
| 68 | Vanishing Gradients and TBPTT | Download Verified |
| 69 | RNN Architectures | Download Verified |
| 70 | LSTM | Download Verified |
| 71 | Why LSTM Works | Download Verified |
| 72 | Deep RNNs and Bi- RNNs | Download Verified |
| 73 | Summary of RNNs | Download Verified |
| 74 | Introduction. | Download Verified |
| 75 | Knn | Download Verified |
| 76 | Binary decision trees | Download Verified |
| 77 | Binary regression trees | Download Verified |
| 78 | Bagging | Download Verified |
| 79 | Random Forest | Download Verified |
| 80 | Boosting | Download Verified |
| 81 | Gradient boosting | Download Verified |
| 82 | Unsupervised learning & Kmeans | Download Verified |
| 83 | Agglomerative clustering | Download Verified |
| 84 | Probability Distributions- Gaussian, Bernoulli | Download Verified |
| 85 | Covariance Matrix of Gaussian Distribution | Download Verified |
| 86 | Central Limit Theorem | Download Verified |
| 87 | Naïve Bayes | Download Verified |
| 88 | MLE Intro | Download Verified |
| 89 | PCA-part 1 | Download Verified |
| 90 | PCA-part 2 | Download Verified |
| 91 | Support Vector Machines | Download Verified |
| 92 | MLE, MAP and Bayesian Regression | Download Verified |
| 93 | Introduction to Generative model | Download Verified |
| 94 | Generative Adversarial Networks (GAN) | Download Verified |
| 95 | Variational Auto-encoders (VAE) | Download Verified |
| 96 | Applications: Cardiac MRI - Segmentation & Diagnosis | Download Verified |
| 97 | Applications: Cardiac MRI Analysis - Tensorflow code walkthrough | Download Verified |
| 98 | Introduction to Week 12 | Download Verified |
| 99 | Application 1 description - Fin Heat Transfer | Download Verified |
| 100 | Application 1 solution | Download Verified |
| 101 | Application 2 description - Computational Fluid Dynamics | Download Verified |
| 102 | Application 2 solution | Download Verified |
| 103 | Application 3 description - Topology Optimization | Download Verified |
| 104 | Application 3 solution | Download Verified |
| 105 | Application 4 - Solution of PDE/ODE using Neural Networks | Download Verified |
| 106 | Summary and road ahead | Download Verified |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Download |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |