5th-Semester

Here is the README.md with links added to the headings:

5th Semester Course Material 📚

This folder contains all the study material, lecture slides, notes, lab manuals, codes, and resources for the courses in the 5th semester of BTech Data Science and Engineering at MIT Manipal.

Whether you're a student looking to ace these courses or just want to learn these topics, you'll find everything you need right here!

Contents

Cloud Computing (CC) ⛅

• L0-Introductory Class.pdf: Introduction to cloud computing concepts
• L5-L7-Virtualization(till ref model).pdf: Basics of virtualization and reference models like NIST
• L8-L12-Hyper_Converged_Infrastructure(updated for case studies).pdf: Study of hyper converged infrastructure and real-world case studies
• L13-L16-VM Provisioning.pdf: Understanding VM provisioning in cloud environments

Deep Learning (DL) 🧠

• Introduction to Deep Learning.pdf: Broad introduction to deep neural networks and applications of DL
• DSE_3151_SLIDE_RNN.pdf: Understanding RNN, sequence modeling, BPTT
• DSE_3151_SLIDE_LSTM_GRU.pdf: Long Short Term Memory networks and Gated Recurrent Units
• DSE_3151_SLIDE_CNN.pdf: Convolutional neural networks for computer vision
• DSE_3151_ENCODER_DECODER_ATTENTION.pdf: Seq-to-seq models, encoder-decoder architecture, attention mechanism
• DSE_3151_SLIDE_TRANSFORMERS.pdf: Transformers and self-attention for NLP tasks

Deep Learning Lab (DL-Lab) 🧪

• DSE 3159 DL Lab Manual 2023.pdf: Lab manual for hands-on neural network experiments
• Week1: Basics of neural networks, loss functions, optimization
• Week2: Building ANN, CNN, RNN for real-world tasks like churn prediction, sentiment analysis etc.
• Week3: Experimenting with different CNN architectures
• Week4: Applying transfer learning on computer vision datasets
• Week5: Time series forecasting using LSTMs
• Week7: Language translation using seq-2-seq LSTMs
• Week8: Text generation using character RNNs
• Week9: Neural machine translation with attention mechanism

Natural Language Processing (NLP) 🗣

• 1_Introduction to the course.pdf: Introduction to NLP tasks like speech recognition, machine translation etc.
• 2_Finite State Automata Regular Expression.pdf: FSMs and regular expressions for sequence modeling
• 3_Morphology and finite state transducers.pdf: Computational morphology and FSTs
• 4_tokenization,stemming,lemmatization.pdf: Basic text processing and normalization techniques
• 5_spelling error_minimum edit distance.pdf: Edit distance algorithms for spelling correction
• 6_N-Grams upto perplexity.pdf: N-gram language models and evaluation metrics like perplexity

Textbook

• Jurafsky, Martin.- Speech and Language Processing_ An Introduction to Natural Language Processing (2007).pdf: Comprehensive book covering all aspects of NLP

Operating Systems (OS) 💻

• DSE_3153_L1_L5.pdf: Introduction to OS, processes, threads, concurrency control
• DSE_3153_L6_L8.pdf: CPU and I/O scheduling, deadlocks
• DSE_3153_L9_L11.pdf: Memory management techniques
• DSE_3153_L12_L14.pdf: File systems, disk scheduling algorithms
• DSE_3153_L15_L16.pdf: Protection, security, virtual machines
• DSE_3153_L17_L19.pdf: Distributed systems concepts

Textbook

• Abraham Silberschatz-Operating System Concepts (9th,2012_12).pdf: Standard textbook for OS covering all key topics

OS Lab (OS-Lab) 🖥️

• Week1: Linux basics, common commands
• Week2: Shell scripting - variables, loops, functions
• Week3: Advanced shell scripting - sed, awk, regular expressions
• Week4: Linux system calls in C - fork, pipes
• Week5: Implementing CPU scheduling algorithms in C
• Lab manuals for all weeks detailing lab exercises

Web Technologies Lab (WT-Lab) 🌐

• week2: HTML basics - images, tables, forms
• week3: CSS - colors, backgrounds, box model
• week4: CSS - animations, transforms, filters
• week5: JavaScript - DOM manipulation, events
• week6: JavaScript - canvas, localStorage, JSON

Overall, this folder contains a goldmine of material covering major 5th sem courses. Go through the organized resources to gain in-depth understanding and clarify all concepts. The codes and lab experiments will help you get practical exposure.