St. Xavier's University Library Search for 'su:"Computer Science"'

(A) First course in computers

]]> By Saxena, Sanjay.
New Delhi Vikas publishing house Pvt Ltd. 2003 .
Various pages 9788125914471

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(A) First course in computers

]]> By Saxena, Sanjay.
New Delhi Vikas publishing house Pvt Ltd. 2003 .
Various pages 9788125914471

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Data analysis using SPSS

]]> By Jasrai, Lokesh.
New Delhi Sage Publications 2020 .
xlii, 401p., I-11 9789353883287

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Data Analysis : using statistics and probability with R Language

]]> By Bishnu, Partha Sarathi .
Delhi PHI Learning private limited 2019 .
xvii, 573 , Preface Acknowledgements 1. Data Analysis—Introduction 2. Basic R Language and MS Excel 3. Descriptive Statistics and Data Visualisation 4. Correlation and Regression Analysis 5. Probability and Probability Distribution 6. Sampling, Sampling Distribution, and Estimation of Parameters 7. Hypothesis Testing and Small Sampling Concepts 8. Analysis of Variance (ANOVA) 9. Chi-Square Test and Different Non-parametric Tests 10. Statistical Quality Control and Acceptance Sampling 11. Time Series Analysis 12. Statistical Decision Analysis Bibliography Index Website Contents • Excel Implementation and R Programs • Additional Topics 9789387472655

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Data Analysis : using statistics and probability with R Language

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(An) Introduction to formal language and automata

]]> By Linz, Peter .
New Delhi Jones and bartlett India pvt. ltd. 2017 .
xiii, 449 , Includes index,appendix & answer 9789384323219

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Theory of computer science : automata, languages and computation

]]> By Mishra, K.L.P .
Delhi PHI learning 2022 .
xiii, 422 , Preface. Notations. 1. Propositions and Predicates. 2. Mathematical Preliminaries. 3. The Theory of Automata. 4. Formal Languages. 5. Regular Sets and Regular Grammars. 6. Context-Free Languages. 7. Pushdown Automata. 8. LR(k) Grammars. 9. Turing Machines and Linear Bounded Automata. 10. Decidability and Recursively Enumerable Languages. 11. Computability. 12. Complexity. Answers to Self-Tests. Solutions (or Hints) to Chapter-end Exercises. Further Reading. Index. 9788120329683

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Operations Research : a textbook for the students of science & technology, economics, accountancy, and management of all universities, professional bodies and civil services

]]> By Swarup, Kanti .
New Delhi Sultan Chand 2022 .
xix, 1156 9789351611837

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(The) Intel microprocessors : 8086/8088, 80186/80188, 80286, 80386, 80486 , pentium, pentium pro processor, pentium II, pentium III, pentium 4, and core2 with 64-bit extensions Architecture, programming, and interfacing

]]> By Brey, Barry B. .
Noida Pearson 2009 .
xviii, 926 , Introduction to the Microprocessor and Computer The Microprocessor and Its Architecture Addressing Modes Data Movement Instructions Arithmetic and Logic Instructions Program Control Instructions Using Assembly Language With C/C++ Programming The Microprocessor 8086/8088 Hardware Specifications Memory Interface Basic I/O Interface Interrupts Direct Memory Access and Dma-Controlled I/O The Arithmetic Coprocessor, Mmx, and Simd Technologies Bus Interface The 80185, 80188, and 80286 Microprocessors The 80386 and 80486 Microprocessors The Pentium and Pentium Pro Microprocessors The Pentium II, Pentium III, Pentium 4, and Core2 Microprocessors 9788131726228

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On the Internet

]]> By Dreyfus, Hubert L..
London Routledge 2001 .
ix, 127p. 9780415228077

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Cryptography and network security principles and practice

]]> By Stallings, Williams.
Chennai Pearson 2023 .
832p. , includes index 9789357059718

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Social network analysis : methods and applications

]]> By Wasserman, Stanley .
New York Cambridge University Press 1994 .
xxxi, 825 9780521387071

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Speech and language processing : An introducing to natural language processing, computational linguistics, and speech recognition

]]> By Jurafsky, Daniel .
Noida Pearson 2014 .
940 , Includes index 9789332518414

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Design and Analysis of Experiments /

]]> By Dean, Angela,.
New York Springer 2017 .
1 online resource (XXV, 840 pages 146 illustrations, 52 illustrations in color.) 9783319522487

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Hands-on machine learning with scikit-learn, keras, and tensorflow : concepts, tools, and techniques to build intelligent systems

]]> By Geron, Aurelien .
New Delhi Shroff publishers & distributors 2022 .
xxv, 834 , Part I. The Fundamentals of Machine Learning 1. The Machine Learning Landscape. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 What Is Machine Learning? 2 Why Use Machine Learning? 2 Examples of Applications 5 Types of Machine Learning Systems 7 Supervised/Unsupervised Learning 7 Batch and Online Learning 14 Instance-Based Versus Model-Based Learning 17 Main Challenges of Machine Learning 23 Insufficient Quantity of Training Data 23 Nonrepresentative Training Data 25 Poor-Quality Data 26 Irrelevant Features 27 Overfitting the Training Data 27 Underfitting the Training Data 29 Stepping Back 30 Testing and Validating 30 Hyperparameter Tuning and Model Selection 31 Data Mismatch 32 Exercises 33 2. End-to-End Machine Learning Project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Working with Real Data 35 iii Look at the Big Picture 37 Frame the Problem 37 Select a Performance Measure 39 Check the Assumptions 42 Get the Data 42 Create the Workspace 42 Download the Data 46 Take a Quick Look at the Data Structure 47 Create a Test Set 51 Discover and Visualize the Data to Gain Insights 56 Visualizing Geographical Data 56 Looking for Correlations 58 Experimenting with Attribute Combinations 61 Prepare the Data for Machine Learning Algorithms 62 Data Cleaning 63 Handling Text and Categorical Attributes 65 Custom Transformers 68 Feature Scaling 69 Transformation Pipelines 70 Select and Train a Model 72 Training and Evaluating on the Training Set 72 Better Evaluation Using Cross-Validation 73 Fine-Tune Your Model 75 Grid Search 76 Randomized Search 78 Ensemble Methods 78 Analyze the Best Models and Their Errors 78 Evaluate Your System on the Test Set 79 Launch, Monitor, and Maintain Your System 80 Try It Out! 83 Exercises 84 3. Classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 MNIST 85 Training a Binary Classifier 88 Performance Measures 88 Measuring Accuracy Using Cross-Validation 89 Confusion Matrix 90 Precision and Recall 92 Precision/Recall Trade-off 93 The ROC Curve 97 Multiclass Classification 100 iv | Table of Contents Error Analysis 102 Multilabel Classification 106 Multioutput Classification 107 Exercises 108 4. Training Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Linear Regression 112 The Normal Equation 114 Computational Complexity 117 Gradient Descent 118 Batch Gradient Descent 121 Stochastic Gradient Descent 124 Mini-batch Gradient Descent 127 Polynomial Regression 128 Learning Curves 130 Regularized Linear Models 134 Ridge Regression 135 Lasso Regression 137 Elastic Net 140 Early Stopping 141 Logistic Regression 142 Estimating Probabilities 143 Training and Cost Function 144 Decision Boundaries 145 Softmax Regression 148 Exercises 151 5. Support Vector Machines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Linear SVM Classification 153 Soft Margin Classification 154 Nonlinear SVM Classification 157 Polynomial Kernel 158 Similarity Features 159 Gaussian RBF Kernel 160 Computational Complexity 162 SVM Regression 162 Under the Hood 164 Decision Function and Predictions 165 Training Objective 166 Quadratic Programming 167 The Dual Problem 168 Kernelized SVMs 169 Table of Contents | v Online SVMs 172 Exercises 174 6. Decision Trees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Training and Visualizing a Decision Tree 175 Making Predictions 176 Estimating Class Probabilities 178 The CART Training Algorithm 179 Computational Complexity 180 Gini Impurity or Entropy? 180 Regularization Hyperparameters 181 Regression 183 Instability 185 Exercises 186 7. Ensemble Learning and Random Forests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Voting Classifiers 189 Bagging and Pasting 192 Bagging and Pasting in Scikit-Learn 194 Out-of-Bag Evaluation 195 Random Patches and Random Subspaces 196 Random Forests 197 Extra-Trees 198 Feature Importance 198 Boosting 199 AdaBoost 200 Gradient Boosting 203 Stacking 208 Exercises 211 8. Dimensionality Reduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 The Curse of Dimensionality 214 Main Approaches for Dimensionality Reduction 215 Projection 215 Manifold Learning 218 PCA 219 Preserving the Variance 219 Principal Components 220 Projecting Down to d Dimensions 221 Using Scikit-Learn 222 Explained Variance Ratio 222 Choosing the Right Number of Dimensions 223 vi | Table of Contents PCA for Compression 224 Randomized PCA 225 Incremental PCA 225 Kernel PCA 226 Selecting a Kernel and Tuning Hyperparameters 227 LLE 230 Other Dimensionality Reduction Techniques 232 Exercises 233 9. Unsupervised Learning Techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Clustering 236 K-Means 238 Limits of K-Means 248 Using Clustering for Image Segmentation 249 Using Clustering for Preprocessing 251 Using Clustering for Semi-Supervised Learning 253 DBSCAN 255 Other Clustering Algorithms 258 Gaussian Mixtures 260 Anomaly Detection Using Gaussian Mixtures 266 Selecting the Number of Clusters 267 Bayesian Gaussian Mixture Models 270 Other Algorithms for Anomaly and Novelty Detection 274 Exercises 275 Part II. Neural Networks and Deep Learning 10. Introduction to Artificial Neural Networks with Keras. . . . . . . . . . . . . . . . . . . . . . . . . . 279 From Biological to Artificial Neurons 280 Biological Neurons 281 Logical Computations with Neurons 283 The Perceptron 284 The Multilayer Perceptron and Backpropagation 289 Regression MLPs 292 Classification MLPs 294 Implementing MLPs with Keras 295 Installing TensorFlow 2 296 Building an Image Classifier Using the Sequential API 297 Building a Regression MLP Using the Sequential API 307 Building Complex Models Using the Functional API 308 Using the Subclassing API to Build Dynamic Models 313 Table of Contents | vii Saving and Restoring a Model 314 Using Callbacks 315 Using TensorBoard for Visualization 317 Fine-Tuning Neural Network Hyperparameters 320 Number of Hidden Layers 323 Number of Neurons per Hidden Layer 325 Learning Rate, Batch Size, and Other Hyperparameters 325 Exercises 327 11. Training Deep Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 The Vanishing/Exploding Gradients Problems 332 Glorot and He Initialization 333 Nonsaturating Activation Functions 335 Batch Normalization 338 Gradient Clipping 345 Reusing Pretrained Layers 345 Transfer Learning with Keras 347 Unsupervised Pretraining 349 Pretraining on an Auxiliary Task 350 Faster Optimizers 351 Momentum Optimization 351 Nesterov Accelerated Gradient 353 AdaGrad 354 RMSProp 355 Adam and Nadam Optimization 356 Learning Rate Scheduling 359 Avoiding Overfitting Through Regularization 364 ℓ1 and ℓ2 Regularization 364 Dropout 365 Monte Carlo (MC) Dropout 368 Max-Norm Regularization 370 Summary and Practical Guidelines 371 Exercises 373 12. Custom Models and Training with TensorFlow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 A Quick Tour of TensorFlow 376 Using TensorFlow like NumPy 379 Tensors and Operations 379 Tensors and NumPy 381 Type Conversions 381 Variables 382 Other Data Structures 383 viii | Table of Contents Customizing Models and Training Algorithms 384 Custom Loss Functions 384 Saving and Loading Models That Contain Custom Components 385 Custom Activation Functions, Initializers, Regularizers, and Constraints 387 Custom Metrics 388 Custom Layers 391 Custom Models 394 Losses and Metrics Based on Model Internals 397 Computing Gradients Using Autodiff 399 Custom Training Loops 402 TensorFlow Functions and Graphs 405 AutoGraph and Tracing 407 TF Function Rules 409 Exercises 410 13. Loading and Preprocessing Data with TensorFlow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 The Data API 414 Chaining Transformations 415 Shuffling the Data 416 Preprocessing the Data 419 Putting Everything Together 420 Prefetching 421 Using the Dataset with tf.keras 423 The TFRecord Format 424 Compressed TFRecord Files 425 A Brief Introduction to Protocol Buffers 425 TensorFlow Protobufs 427 Loading and Parsing Examples 428 Handling Lists of Lists Using the SequenceExample Protobuf 429 Preprocessing the Input Features 430 Encoding Categorical Features Using One-Hot Vectors 431 Encoding Categorical Features Using Embeddings 433 Keras Preprocessing Layers 437 TF Transform 439 The TensorFlow Datasets (TFDS) Project 441 Exercises 442 14. Deep Computer Vision Using Convolutional Neural Networks. . . . . . . . . . . . . . . . . . . 445 The Architecture of the Visual Cortex 446 Convolutional Layers 448 Filters 450 Stacking Multiple Feature Maps 451 Table of Contents | ix TensorFlow Implementation 453 Memory Requirements 456 Pooling Layers 456 TensorFlow Implementation 458 CNN Architectures 460 LeNet-5 463 AlexNet 464 GoogLeNet 466 VGGNet 470 ResNet 471 Xception 474 SENet 476 Implementing a ResNet-34 CNN Using Keras 478 Using Pretrained Models from Keras 479 Pretrained Models for Transfer Learning 481 Classification and Localization 483 Object Detection 485 Fully Convolutional Networks 487 You Only Look Once (YOLO) 489 Semantic Segmentation 492 Exercises 496 15. Processing Sequences Using RNNs and CNNs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Recurrent Neurons and Layers 498 Memory Cells 500 Input and Output Sequences 501 Training RNNs 502 Forecasting a Time Series 503 Baseline Metrics 505 Implementing a Simple RNN 505 Deep RNNs 506 Forecasting Several Time Steps Ahead 508 Handling Long Sequences 511 Fighting the Unstable Gradients Problem 512 Tackling the Short-Term Memory Problem 514 Exercises 523 16. Natural Language Processing with RNNs and Attention. . . . . . . . . . . . . . . . . . . . . . . . 525 Generating Shakespearean Text Using a Character RNN 526 Creating the Training Dataset 527 How to Split a Sequential Dataset 527 Chopping the Sequential Dataset into Multiple Windows 528 x | Table of Contents Building and Training the Char-RNN Model 530 Using the Char-RNN Model 531 Generating Fake Shakespearean Text 531 Stateful RNN 532 Sentiment Analysis 534 Masking 538 Reusing Pretrained Embeddings 540 An Encoder–Decoder Network for Neural Machine Translation 542 Bidirectional RNNs 546 Beam Search 547 Attention Mechanisms 549 Visual Attention 552 Attention Is All You Need: The Transformer Architecture 554 Recent Innovations in Language Models 563 Exercises 565 17. Representation Learning and Generative Learning Using Autoencoders and GANs. 567 Efficient Data Representations 569 Performing PCA with an Undercomplete Linear Autoencoder 570 Stacked Autoencoders 572 Implementing a Stacked Autoencoder Using Keras 572 Visualizing the Reconstructions 574 Visualizing the Fashion MNIST Dataset 574 Unsupervised Pretraining Using Stacked Autoencoders 576 Tying Weights 577 Training One Autoencoder at a Time 578 Convolutional Autoencoders 579 Recurrent Autoencoders 580 Denoising Autoencoders 581 Sparse Autoencoders 582 Variational Autoencoders 586 Generating Fashion MNIST Images 590 Generative Adversarial Networks 592 The Difficulties of Training GANs 596 Deep Convolutional GANs 598 Progressive Growing of GANs 601 StyleGANs 604 Exercises 607 18. Reinforcement Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 Learning to Optimize Rewards 610 Policy Search 612 Table of Contents | xi Introduction to OpenAI Gym 613 Neural Network Policies 617 Evaluating Actions: The Credit Assignment Problem 619 Policy Gradients 620 Markov Decision Processes 625 Temporal Difference Learning 629 Q-Learning 630 Exploration Policies 632 Approximate Q-Learning and Deep Q-Learning 633 Implementing Deep Q-Learning 634 Deep Q-Learning Variants 639 Fixed Q-Value Targets 639 Double DQN 640 Prioritized Experience Replay 640 Dueling DQN 641 The TF-Agents Library 642 Installing TF-Agents 643 TF-Agents Environments 643 Environment Specifications 644 Environment Wrappers and Atari Preprocessing 645 Training Architecture 649 Creating the Deep Q-Network 650 Creating the DQN Agent 652 Creating the Replay Buffer and the Corresponding Observer 654 Creating Training Metrics 655 Creating the Collect Driver 656 Creating the Dataset 658 Creating the Training Loop 661 Overview of Some Popular RL Algorithms 662 Exercises 664 19. Training and Deploying TensorFlow Models at Scale. . . . . . . . . . . . . . . . . . . . . . . . . . . 667 Serving a TensorFlow Model 668 Using TensorFlow Serving 668 Creating a Prediction Service on GCP AI Platform 677 Using the Prediction Service 682 Deploying a Model to a Mobile or Embedded Device 685 Using GPUs to Speed Up Computations 689 Getting Your Own GPU 690 Using a GPU-Equipped Virtual Machine 692 Colaboratory 693 Managing the GPU RAM 694 xii | Table of Contents Placing Operations and Variables on Devices 697 Parallel Execution Across Multiple Devices 699 Training Models Across Multiple Devices 701 Model Parallelism 701 Data Parallelism 704 Training at Scale Using the Distribution Strategies API 709 Training a Model on a TensorFlow Cluster 711 Running Large Training Jobs on Google Cloud AI Platform 714 Black Box Hyperparameter Tuning on AI Platform 716 Exercises 717 Thank You! 718 A. Exercise Solutions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719 B. Machine Learning Project Checklist. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755 C. SVM Dual Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761 D. Autodiff. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765 E. Other Popular ANN Architectures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773 F. Special Data Structures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783 G. TensorFlow Graphs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 801 9789355421982

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Data Analysis : using statistics and probability with R Language

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Data structures and algorithms using c : theory, design and implementation

]]> By Salaria, R.S..
New Delhi Khanna 2024 .
522p. , includes index 9789381068588

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(The) Rise and influence of Microgenres in the Digital age /

By Jain, Nityaa.
Kolkata: St. Xavier's University, 2025 .
43p.

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Tableau Desktop cookbook : quick & simple recipes to help you navigate Tableau desktop

]]> By Brown, Lorna .
Navi Mumbai Shroff 2021 .
xvii, 665 , Includes index 9789355427311

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Introduction to it systems

]]> By Roy, Nilanjana Dutta.
Kolkata Aryan 2023 .
312p. , includes index 9788195189885

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Computer aptitude for competitive examination

]]> By Thareja, Reema.
Kolkata Univertsity press 2024 .
191p. , 1. Introduction to Computer 2. GUI-based Operating Systems 3. Data Organization and Database Management System 4. Internet, WWW and Web Browsers 5. Communication and Collaboration 6. Application of Digital Financial Services 7. IT and Its Application in Business 8. Data Security and Encryption 9. Elements of Word Processing 10. Spreadsheet 11. Microsoft PowerPoint 12. Microsoft Access Solved Papers 1–9 MCQ Sets 1–4 On the Smart App Solved Question Papers for • Banking • SSC • Railways • Police • Other Government Competitive Examinations 9789389211948

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Gate and Pgecet : computer science and information technolgy

By Ramaiah K, Dasaradh.
Delhi PHI 2020 .
xii, various pages , Preface Acknowledgements How to Prepare for Exam What is Special about this Book How to Read this Book PART A TECHNICAL SECTION Chapter 1: Theory of Computation Chapter 2: Compiler Design Chapter 3: Digital Logic Design Chapter 4: Computer Organization and Architecture Chapter 5: Computer Networks Chapter 6: Database Management System Chapter 7: Operating System Concepts Chapter 8: Fundamentals of C Programming Chapter 9: Data Structures Chapter 10: Design and Analysis of Algorithms PART B GENERAL APTITUDE Chapter 11: General Aptitude Verbal Ability Chapter 12: Numerical Aptitude PART C ENGINEERING MATHEMATICS Chapter 13: Basic Mathematics Chapter 14: Discrete Mathematics GATE 2019 Question Paper with Answer Keys 979389347340

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