Compiler Design Fundamentals: Phases, Parsing, and Optimization Techniques
Phases of a Compiler Design Process
A compiler converts source code into machine code. The main phases are:
- Lexical Analysis: Breaks code into tokens and removes spaces/comments.
- Syntax Analysis: Checks the grammar structure of tokens.
- Semantic Analysis: Checks meaning, types, and logical correctness.
- Intermediate Code Generation: Creates machine-independent middle code.
- Optimization: Improves code efficiency.
- Code Generation: Converts intermediate code into machine code.
- Symbol Table & Error Handling:
Machine Learning Fundamentals: Core Algorithms and Techniques
Rule-Based Classification Technique
Rule-based classification is a data mining method where IF–THEN rules are used to classify data into different categories. Each rule has two parts:
- IF (condition): tests certain attribute values
- THEN (conclusion): assigns a class label
Example:
IF age > 18 AND income = high THEN class = “Premium Customer”.
Key Points of Rule-Based Classification
- Simple and Interpretable: Rules are easy to understand because they are written in natural language.
- Classification
Key NP-Complete Problem Reductions: SAT, Clique & More
CircuitSAT to SAT Reduction
Problem Checks
The Boolean Satisfiability Problem (SAT) is in NP because a given Boolean assignment serves as a polynomial-size certificate, which can be verified by evaluating the formula in polynomial time.
Reduction Construction
Given a circuit composed exclusively of NAND gates, the reduction proceeds as follows:
- Create a Boolean variable for each circuit input and for the output of each gate.
- For each gate represented as
z = NAND(a,b), add Conjunctive Normal Form (CNF)
Data Science Core Concepts: Workflow, Models, and Analytics
Data Science Fundamentals and Applications
What is Data Science?
Data Science is a field that uses mathematics, programming, and other techniques to analyze data and discover insights. It helps businesses make better decisions.
Types of Data Science Disciplines
- Data Engineering: Involves designing, building, and managing data infrastructure to help data scientists analyze data.
- Data Analysis: Involves using statistical analysis to answer questions about a business’s data.
- Predictive Analytics: Uses historical
Deep Learning Architectures: CNNs, RNNs, Autoencoders, and NLP
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks, or CNNs, are a special class of deep learning models designed primarily for analyzing visual data such as images and videos. Unlike traditional fully connected neural networks, CNNs are built to automatically and adaptively learn spatial hierarchies of features through convolution operations.
Core Components of a CNN
- Convolutional Layers: Apply small filters (also called kernels) that slide across the input image to detect features
Database Concurrency, Normalization, and PL/pgSQL Programming
Transaction Concurrency: Preventing the Lost Update Problem
The Lost Update Problem occurs when two transactions simultaneously update the same data, resulting in one update overwriting the changes made by the other. This concurrency issue leads to data inconsistency.
It can be prevented using locking mechanisms, such as row-level exclusive locks, which ensure that only one transaction can modify the specific data item at any given time.
Core Database Concepts: True or False Statements
Using procedural