Computer Science is the study of computers, computational systems, and the principles behind how they work. It focuses on how data is processed, how software and hardware interact, and how problems can be solved efficiently using algorithms and programming. In simple terms, Computer Science = the science of solving problems using computers.
- Introduction to Computer Science
- History of Computing
- Number Systems (Binary, Octal, Decimal, Hexadecimal)
- Boolean Algebra and Logic Gates
- Algorithms & Flowcharts
- Computer Architecture Basics (CPU, Memory, I/O, Storage)
- Operating System Fundamentals
- Software vs Hardware Concepts
- Programming Paradigms (Procedural, Object-Oriented, Functional)
- Variables, Data Types, and Operators
- Control Structures (if, switch, loops)
- Functions / Methods
- Arrays and Strings
- Recursion
- File Handling
- Error Handling and Debugging
- Object-Oriented Programming (OOP) — Classes, Objects, Inheritance, Polymorphism, Encapsulation, Abstraction
- Functional Programming Basics
- Arrays and Linked Lists
- Stacks and Queues
- Trees (Binary, BST, AVL, Heap)
- Graphs (BFS, DFS, Shortest Path)
- Hashing
- Sorting Algorithms (Bubble, Merge, Quick, Insertion, Selection)
- Searching Algorithms (Linear, Binary)
- Time & Space Complexity (Big O Notation)
- Dynamic Programming
- Greedy Algorithms
Data Structures and Algorithms - details
Data Structures and Algorithms (DSA) is a foundational concept in computer science that helps in solving problems efficiently. A data structure is a way of organizing and storing data so that it can be accessed and modified efficiently. An algorithm is a clear, step-by-step set of instructions or rules designed to solve a specific problem or perform a task.
1] Linear - Array, Linked List, Stack, Queue -> Store sequential data
2] Non-linear - Tree, Graph -> Represent hierarchy or networks
3] Hash-based - Hash Table, Hash Map -> Fast lookups and insertions
4] Specialized - Heap, Trie, Segment Tree -> For advanced operations like prefix matching or efficient searching
1] Sorting: Bubble Sort, Merge Sort, Quick Sort
2] Searching: Linear Search, Binary Search
3] Recursion: Solving problems by breaking them into subproblems
4] Dynamic Programming: Optimizing recursive problems
5] Greedy Algorithms: Making locally optimal choices
6] Graph Algorithms: BFS, DFS, Dijkstra’s, Kruskal’s
1] Efficient Code - Helps write optimized and scalable programs
2] Problem Solving - Breaks down complex problems into manageable steps
3] Interviews - Most tech interviews are based on DSA
4] Competitive Programming - Backbone for contests like Codeforces, LeetCode
5] Real-World Applications - Used in databases, OS, compilers, AI, etc.
This repository contains implementations of various Data Structures and Algorithms (DSA) using Python. It is structured to help beginners and professionals improve their coding skills for technical interviews and competitive programming.
Well-structured DSA implementations, Beginner-friendly explanations, Covers both theory and coding problems, Ideal for interview preparation and competitive coding.
🚀 Practice DSA — Strengthen your problem-solving skills with hands-on coding challenges! Solve, learn, and master Data Structures & Algorithms.
- Sets, Relations, and Functions
- Propositional & Predicate Logic
- Proof Techniques (Induction, Contradiction, etc.)
- Graph Theory
- Combinatorics & Probability
- Number Theory & Cryptography Basics
- CPU Design & Instruction Cycle
- Memory Hierarchy (Cache, RAM, ROM)
- Pipelining and Parallelism
- Input/Output Systems
- Assembly Language Basics
- Process Management
- Threads & Concurrency
- CPU Scheduling Algorithms
- Deadlocks
- Memory Management & Paging
- File Systems
- System Calls
- Virtualization Basics
- Network Models (OSI & TCP/IP)
- IP Addressing & Subnetting
- Network Devices (Router, Switch, Hub)
- Protocols (HTTP, FTP, DNS, DHCP, SMTP, etc.)
- Transmission Media
- Network Security Basics
- Wireless & Mobile Networks
- Introduction to Databases
- ER Model & Relational Model
- Normalization
- SQL Queries (SELECT, JOIN, GROUP BY, etc.)
- Transactions & Concurrency Control
- Indexing
- NoSQL Databases Overview
- Information Security Fundamentals
- Cryptographic Algorithms (Symmetric, Asymmetric, Hashing)
- Public Key Infrastructure (PKI)
- Digital Signatures & Certificates
- Network Security (Firewalls, IDS/IPS, VPNs)
- Cyber Forensics Basics
- Software Development Life Cycle (SDLC)
- Agile, Scrum, and Waterfall Models
- Software Design Principles (SOLID, DRY, KISS)
- UML Diagrams
- Testing & Quality Assurance
- Version Control (Git & GitHub)
- DevOps Introduction
Automation - details
Automation is the use of technology to perform tasks with minimal human intervention. It aims to increase efficiency, accuracy, and speed by automating repetitive or complex operations that would otherwise require manual effort.
A Python-based automation tool that sends WhatsApp messages using libraries like
pywhatkit,twilio,pyautogui, andwebdriver.
This program automates the process of sending messages to WhatsApp contacts directly from the desktop.
This project contains multiple automation scripts. Some scripts depend on browser drivers or third-party APIs and may require additional setup.Deskapp.py: A desktop application-based approach for automating messages seamlessly.
whatkit.py: A web-based solution offering precise and efficient message automation.
1] Software Automation - Automating digital tasks and workflows (e.g., Test automation, data scraping, bot workflows)
2] Industrial Automation - Use of machinery and control systems in manufacturing (e.g., Assembly lines, robotic arms)
3] IT Automation - Automating infrastructure and IT processes (e.g., Server provisioning, backups, CI/CD pipelines)
4] Business Process Automation (BPA) - Streamlining business workflows (e.g., Invoice processing, HR onboarding)
5] Robotic Process Automation (RPA) - Using bots to mimic human interactions with digital systems (e.g., Automating form filling, report generation)
1] Selenium – Open-source framework for automating web applications across browsers and platforms.
2] Cypress – JavaScript-based end-to-end testing framework for modern web applications.
3] Playwright – Microsoft-developed tool for cross-browser end-to-end testing.
4] Appium – Automates native, mobile web, and hybrid apps on iOS and Android.
5] TestComplete – Commercial tool for desktop, web, and mobile app testing.
6] Tricentis Tosca – Enterprise-grade model-based automation tool with risk-based testing.
7] Robot Framework – Generic open-source framework for acceptance testing and RPA.
8] Katalon Studio – All-in-one test automation solution for web, API, mobile, and desktop.
9] Ranorex – GUI test automation for desktop, web, and mobile applications.
10] TestCafe – Node.js-based tool for end-to-end web testing.
Robotic Process Automation (RPA)
1] UiPath – RPA tool for automating repetitive, rule-based tasks.
2] Automation Anywhere – Enterprise-focused RPA platform.
3] Blue Prism – Scalable RPA solution for complex business processes.
DevOps & CI/CD Automation
1] Jenkins – Automation server for building, testing, and deploying code.
2] GitHub Actions – CI/CD and workflow automation for GitHub projects.
3] GitLab CI/CD – Integrated CI/CD in GitLab for streamlined DevOps.
4] CircleCI – Platform for automating development workflows.
5] Azure DevOps – End-to-end DevOps tools by Microsoft.
Infrastructure as Code (IaC)
1] Terraform – Declarative IaC tool for provisioning infrastructure.
2] Ansible – Tool for configuration management and automation.
3] Chef – Automates infrastructure configuration and deployment.
4] Puppet – Manages and automates software configurations.
2] Behavior-Driven Development (BDD) – Combines TDD with domain-driven design.
3] CI/CD Practices – Ensures frequent and reliable code deployments.
4] Infrastructure Automation – Automating provisioning and configuration.
5] Monitoring & Logging – Tracking system performance and analyzing logs.
6] Security Automation – Automates threat detection and response.
1] Software Testing - Selenium, Cypress, Playwright, Appium
2] IT & DevOps - Jenkins, Ansible, Terraform, GitHub Actions
3] RPA - UiPath, Automation Anywhere, Blue Prism
4] Business Automation - Zapier, Power Automate, Nintex
5] Monitoring & Alerting - Nagios, Prometheus, Grafana
1] Test Automation - Automating software tests (unit, integration, UI)
2] CI/CD Pipelines - Automatically build, test, and deploy software
3] Infrastructure as Code (IaC) - Automatically manage and provision infrastructure
4] Business Workflow Automation - Automate repetitive business tasks
5] Security Automation - Automate vulnerability scanning, logging, and alerting
6] Data Automation - Automate data collection, analysis, and reporting
1] Automatically sending confirmation emails when a form is submitted
2] Robotic arms assembling car parts in a factory
3] Running automated test suites on every pull request
4] Automatically updating inventory in an e-commerce system
✅ Reduce human error
✅ Save time and labor costs
✅ Increase productivity and consistency
✅ Free up human workers for more strategic tasks
Microsoft .NET - details
.NET is a free, open-source, cross-platform developer platform created by Microsoft for building many different types of applications. With .NET, you can use multiple languages, editors, and libraries to build.
.NET is a free, open-source, cross-platform developer platform created by Microsoft for building many different types of applications. With .NET, you can use multiple languages, editors, and libraries to build.
1] .NET SDK - A software development kit that includes compilers, libraries, and CLI tools.
2] CLR (Common Language Runtime) - The virtual machine component that handles memory management, type safety, and exception handling.
3] BCL (Base Class Library) - A large collection of pre-built libraries for common functions like file handling, database access, XML manipulation, and more.
4] Languages - C#, F#, and Visual Basic are officially supported.
6] ASP.NET Core - A powerful framework for building web applications and APIs.
7] Entity Framework (EF) - Core An Object-Relational Mapper (ORM) for working with databases using .NET objects.
8] MAUI (Multi-platform App UI) - Used for building cross-platform mobile and desktop apps with a single codebase.
9] Blazor - Enables building interactive web UIs using C# instead of JavaScript.
1] Web Apps - ASP.NET Core, Blazor
2] Desktop Apps - Windows Forms, WPF, MAUI
3] Mobile Apps - .NET MAUI, Xamarin
4] Cloud - Azure with .NET
5] IoT - .NET on Raspberry Pi and ARM devices
6] Games - Unity with C#
7] AI/ML - ML.NET
✅ Cross-platform: Build and run apps on Windows, macOS, and Linux.✅ Language support: Use C#, F#, or VB.NET.
✅ High performance: Especially with ASP.NET Core.
✅ Scalable and flexible: Ideal for microservices and enterprise-level applications.
✅ Active ecosystem: Supported by Microsoft and a large open-source community.
1] .NET SDK & CLI – Essential for building, running, and publishing .NET applications. Includes tools like dotnet build, dotnet run, and dotnet publish.
2] MSBuild – Microsoft's build engine for compiling code, packaging, and deploying applications.
3] Visual Studio – The primary IDE for .NET development, offering robust features for coding, debugging, and deployment.
1] Common Language Runtime (CLR) – Manages code execution, memory, and security.
2] Base Class Library (BCL) – Provides fundamental classes for collections, file I/O, data types, and more.
3] Assemblies – Compiled code libraries used for deployment, versioning, and security.
1] ASP.NET Core – Framework for building web applications and APIs.
2] Entity Framework (EF) – Object-relational mapper (ORM) for data access.
3] Windows Presentation Foundation (WPF) – For building Windows desktop applications with rich UI.
4] Windows Communication Foundation (WCF) – Framework for building service-oriented applications.
5] ML.NET – Machine learning framework for .NET developers.
6] ADO.NET – Data access technology for interacting with databases.
1] xUnit.net – Popular unit testing framework for .NET applications.
2] NDepend – Static analysis tool for measuring code quality and dependencies.
3] dotnet test – Command-line tool for running tests in .NET projects.
1] Authentication & Authorization – Implementing secure user access and permissions.
2] Data Protection – Safeguarding sensitive data within applications.
3] HTTPS Enforcement – Ensuring secure communication over the web.
1] LINQ (Language Integrated Query) – Querying data in a type-safe manner.
2] Dependency Injection (DI) – Design pattern for achieving Inversion of Control.
3] Microservices Architecture – Building applications as a collection of small services.
1] dotnet tool list – Command to list installed .NET tools.
2] Third-Party Tools – Explore tools like ReSharper, PostSharp, and others for enhanced development experience.
- Automata Theory (DFA, NFA, PDA)
- Regular Expressions
- Context-Free Grammars
- Turing Machines
- Decidability and Complexity Classes
- Phases of Compiler
- Lexical Analysis & Syntax Analysis
- Parsing Techniques
- Code Optimization & Code Generation
- Introduction to AI
- Types of AI (Narrow, General, Super)
- Machine Learning Basics (Supervised, Unsupervised, Reinforcement)
- Neural Networks
- Natural Language Processing (Basics)
ML and DL - details
Machine Learning (ML) is a branch of artificial intelligence that enables computers to learn from data and improve their performance on tasks without being explicitly programmed. Instead of following fixed rules, ML systems identify patterns in data and use these patterns to make predictions, classify information, or make decisions
2] Mathematics for ML – Linear Algebra, Probability & Statistics, Calculus (gradients, optimization)
3] Fundamental Algorithms – Linear & Logistic Regression, Decision Trees, Random Forests, SVM, k-NN, Naive Bayes
4] Clustering & Dimensionality Reduction – K-Means, DBSCAN, PCA, t-SNE
5] Data Preprocessing & Feature Engineering – Data cleaning, encoding, scaling, feature selection
6] Model Evaluation & Validation – Confusion matrix, precision, recall, F1-score, ROC-AUC, cross-validation
7] Hyperparameter Tuning – Grid search, random search, Bayesian optimization, regularization
8] Reinforcement Learning (Basics) – Markov Decision Processes, Q-Learning, Policy Gradients
9] Natural Language Processing (NLP) – Text preprocessing, vectorization, sentiment analysis, chatbots
10] Model Deployment & MLOps – Model serialization, containerization (Docker), monitoring, scaling
Deep Learning is a specialized area of machine learning that uses algorithms called artificial neural networks to model and solve complex problems. Inspired by the human brain’s structure, deep learning networks have many layers (“deep” networks) that can automatically learn features and patterns from large amounts of data.
2] Neural Networks Fundamentals – Perceptrons, feedforward networks, activation functions (ReLU, Sigmoid, Tanh)
3] Training Neural Networks – Loss functions (MSE, Cross-Entropy), backpropagation, optimizers (SGD, Adam)
4] Convolutional Neural Networks (CNNs) – Layers, pooling, architectures (LeNet, AlexNet, VGG, ResNet), image tasks
5] Recurrent Neural Networks (RNNs) – Sequence modeling, LSTM, GRU, time-series & NLP applications
6] Transformer Models & Attention – Self-attention, BERT, GPT for NLP and beyond
7] Generative Models – Autoencoders, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs)
8] Transfer Learning – Pre-trained models, fine-tuning for new tasks
9] Regularization & Optimization – Dropout, batch norm, L1/L2 regularization, early stopping
10] Hyperparameter Tuning – Learning rates, batch sizes, grid/random/Bayesian search
11] Deep Learning Deployment – TensorFlow Serving, ONNX, TorchScript, model quantization
1] Python – Primary language for ML/DL due to its simplicity and vast ecosystem of libraries.
2] NumPy – Fundamental package for numerical computing with support for large multidimensional arrays and matrices.
3] Pandas – Data manipulation and analysis library offering powerful data structures like DataFrames.
4] Matplotlib – Visualization library used for plotting data and creating graphs and charts.
5] Scikit-learn – ML library offering tools for classification, regression, clustering, and model selection.
6] TensorFlow – Open-source deep learning framework developed by Google for building and deploying ML models.
7] Keras – High-level API running on top of TensorFlow, designed for fast experimentation and prototyping.
8] PyTorch – Flexible and popular deep learning framework developed by Facebook for research and production.
9] OpenCV – Computer vision library for image and video analysis tasks such as object detection and recognition.
10] Jupyter Notebook – Interactive environment for writing and running code, visualizations, and notes in one place.
11] Google Colab – Free cloud-based Jupyter notebook environment with GPU support, ideal for ML/DL experiments.
12] Hugging Face – Platform and library for state-of-the-art transformer models for NLP, vision, and more.
13] MLflow – Open-source platform for managing the ML lifecycle including experimentation, reproducibility, and deployment.
14] Docker – Containerization tool that packages ML/DL applications and dependencies for portability and scalability.
15] ONNX – Open format to represent deep learning models, enabling cross-framework compatibility.
16] Weights & Biases (W&B) – Tool for tracking experiments, visualizing metrics, and collaborating on ML/DL projects.
Generative AI - details
Generative AI refers to a class of artificial intelligence models designed to create new content such as text, images, audio, or video by learning patterns from existing data. These models, like GANs, VAEs, and Transformers, can generate realistic and creative outputs that mimic human-like creativity. Applications range from writing and art generation to code synthesis and music composition.
1] Fundamentals of Generative AI : Learn how AI models create new data like text, images, audio, and more from patterns in training data.
2] Text, Image, Audio, Video, and Code Generation : Explore how AI systems generate content across multiple modalities using deep learning techniques.
3] GANs (Generative Adversarial Networks) : Use two neural networks in competition to produce highly realistic synthetic data.
4] VAEs (Variational Autoencoders) : Learn how VAEs encode data into a latent space and decode it for controlled and smooth data generation.
5] Diffusion Models : Generate high-quality images by reversing a noise-based degradation process through iterative denoising.
6] Transformers : Foundation of modern generative AI, leveraging self-attention for sequential data generation in models like GPT and BERT.
7] Deepfakes and Ethics : Understand the ethical implications and risks of synthetic media that mimics real people or voices.
8] BLEU (Bilingual Evaluation Understudy) : Measures n-gram overlap between generated and reference text, often used in machine translation.
9] ROUGE (Recall-Oriented Understudy for Gisting Evaluation) : Evaluates the recall of overlapping phrases in generated summaries compared to references.
10] FID (Fréchet Inception Distance) : Quantifies image quality by comparing the feature distribution of real and generated images.
11] Inception Score : Evaluates image generation by assessing both object recognizability and output diversity.
12] Applications in Art, Music, Content, Code, Avatars : Generative AI is driving innovation in creativity, enabling tools for art, music composition, coding, and virtual avatars.
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1] Hugging Face Transformers : A leading library for using and training state-of-the-art NLP and multimodal transformer models.
2] Diffusers : A Hugging Face library for implementing diffusion models for high-quality image and media generation.
3] OpenAI API : Provides access to GPT, DALL·E, Whisper, and other powerful foundation models via API.
4] Runway ML : A no-code platform for creatives to use generative AI models in design, art, and video.
5] Gradio : Simplifies ML model deployment by allowing developers to build interactive UIs in just a few lines of code.
6] Replicate : Enables running and sharing ML models in the cloud without infrastructure setup.
7] TensorFlow : An open-source deep learning framework for building and training scalable machine learning models.
8] PyTorch : A flexible and developer-friendly deep learning library widely used for research and production.
Large Language Models (LLMs) are advanced AI models trained on vast amounts of text data to understand and generate human-like language. They use architectures like Transformers to predict and produce coherent text, enabling tasks such as translation, summarization, question-answering, and conversation. Examples include GPT, LLaMA, and PaLM. LLMs power many modern natural language applications and conversational AI systems.
1] Transformer Architecture & Self-Attention : Core deep learning model using attention to process sequences efficiently and capture context.
2] Pretraining & Fine-tuning (LoRA, PEFT, RLHF) : Techniques to adapt large models for specific tasks by efficient training and reinforcement learning.
3] Prompt Engineering (Zero-shot, Few-shot, CoT) : Designing effective input prompts to guide language models’ responses without extensive retraining.
4] Evaluation Metrics (Perplexity, LAMBADA, TruthfulQA) : Quantitative measures to assess language model performance and truthfulness on complex tasks.
5] Model Deployment, Scalability, and Cost Estimation : Strategies to efficiently serve models at scale while managing computational resources and expenses.
6] RAG (Retrieval Augmented Generation) : Combining retrieval systems with generative models to improve answer accuracy using external knowledge.
7] Ethics: Hallucination, Security, Jailbreaking : Addressing risks of misinformation, system vulnerabilities, and adversarial exploitation in AI models.
1] Hugging Face Models : Offers thousands of open-source pre-trained models for NLP, vision, audio, and more.
2] LangChain : A framework to build LLM-powered apps by chaining prompts, tools, and memory together.
3] OpenAI GPT-3.5/4 : Leading proprietary large language models with world-class reasoning and generation capabilities.
4] Meta LLaMA 2 / 3 : Open-weight transformer models built by Meta for research and commercial use.
5] Claude (Anthropic) : Constitutional AI-based LLM with strong reasoning, harmlessness, and helpfulness principles.
6] Google Gemini : Multimodal foundation model from DeepMind capable of text, vision, and code understanding.
7] Mistral AI : Efficient, high-performance open-weight LLMs optimized for real-world deployment.
8] Haystack : Powerful framework for building retrieval-augmented generation (RAG) pipelines using LLMs.
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Language and Communication Models are AI systems that extend beyond text understanding to include human communication aspects like speech, emotion, and multimodal inputs (e.g., audio, video, and text). They power technologies such as speech recognition, text-to-speech, conversational agents, and emotion-aware AI, enabling more natural and context-aware interactions between humans and machines.
1] Language vs. Communication Models : Understanding the distinction between structured language models and broader human communication patterns.
2] Speech, Text, and Emotion as Modalities : Key modalities processed by AI to understand and generate human-like interactions.
3] Pragmatics, Semantics, and Context-awareness : How AI interprets meaning, tone, and context beyond literal text.
1] Conversational AI & Dialogue Systems : AI agents designed to engage in meaningful, coherent conversations with users.
2] Speech-to-Text (ASR) : Converts spoken audio into textual data for analysis and response.
3] Text-to-Speech (TTS) : Converts written text into natural-sounding human speech.
4] Emotion & Sentiment Recognition : Detects affective states in voice or text to tailor responses.
5] Multimodal Language Understanding : Combines input like video, audio, and text to enable richer AI understanding.
1] Transformer-based speech models : Models using self-attention to process audio sequences effectively.
2] Audio Transformers (Whisper, SpeechT5) : Advanced models designed for speech recognition, translation, and synthesis.
3] Multimodal Fusion (Gemini, GPT-4o, SeamlessM4T) : Combines modalities (audio, visual, text) in a single unified model.
4] Reinforcement Learning for Dialog Control : Uses reward mechanisms to optimize interactive conversations.
5] Attention-based ASR/TTS systems : Employs attention mechanisms for accurate speech recognition and synthesis.
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1] Naturalness and Fluency of Speech : Evaluates how human-like and fluid the generated speech sounds.
2] Emotion Detection Accuracy : Measures how well the model captures human emotional states.
3] BLEU, METEOR, BERTScore : Text-level evaluation metrics for measuring generated vs. reference quality.
4] Human Evaluation: Engagement & Clarity : Real-user feedback to judge interaction quality and coherence.
1] OpenAI Whisper : A powerful, open-source speech-to-text model for accurate transcription.
2] SpeechT5 : A versatile model supporting both text-to-speech and speech-to-text tasks.
3] Coqui TTS : An open-source framework for high-quality text-to-speech synthesis.
4] Mozilla DeepSpeech : RNN-based speech recognition system inspired by Baidu’s Deep Speech research.
5] Rasa : Open-source conversational AI platform combining NLP and machine learning for chatbots.
6] Google Gemini : Multimodal large communication model integrating multiple data types for advanced AI.
7] Meta SeamlessM4T : Multilingual, multimodal translation system supporting speech, text, and vision.
8] Azure Speech Service : Cloud-based service offering speech recognition, synthesis, and translation APIs.
| Feature | Large Language Models | Language & Communication Models |
|---|---|---|
| Modality | Text-only | Text + Speech + Emotion + Multimodal |
| Focus | Text generation and understanding | Human-like communication and interaction |
| Applications | Chatbots, summarization, RAG | Voice assistants, translators, emotion-aware AI |
| Technologies | Transformers, RAG | Transformers + ASR + TTS + Fusion Models |
Large Concept Models (LCMs) are generalist AI systems trained on multimodal and multi-domain data to learn abstract concepts, reason across modalities, and perform cross-task generalization. These models go beyond language, integrating text, audio, vision, and code into a unified conceptual framework. Examples include GPT-4o, Gemini, Claude, and SeamlessM4T.
1] Concept Learning & Abstraction : Understanding symbolic reasoning, world knowledge, and abstract concept mapping across domains.
2] Multimodal Input/Output Fusion : Integrating text, image, audio, and video using cross-attention and shared embeddings.
3] Generalist Intelligence & Tool Use : Designing systems that perform multi-domain tasks with reasoning, planning, and memory.
4] Multimodal Architectures (MoE, Flamingo, Gemini, GPT-4o) : Vision-language-audio models using expert routing and joint representations.
5] Constitutional & Ethical Reasoning : Human-aligned learning with ethical filters and safety policies (e.g., Claude, Gemini).
6] Evaluation Benchmarks (MMMU, VQAv2, MMLU, TDI-Eval) : Testing reasoning, factuality, and cross-modal comprehension.
7] Cross-Modal Dialogue & Emotion Understanding : Coherent, emotionally aware responses across speech, text, and images.
1] GPT-4o (OpenAI) : Multimodal unified model handling text, vision, and speech in real time.
2] Gemini (Google DeepMind) : Conceptual agent with tool use, reasoning, and multimodal interaction.
3] Claude (Anthropic) : Constitutional model with safety alignment and cross-modal grounding.
4] Meta SeamlessM4T : Speech-to-speech translation with multilingual and multimodal fusion.
5] Flamingo : Few-shot vision-language model from DeepMind.
6] LLaVA : Visual-Language Assistant (open-source) for VL tasks.
7] Hugging Face Transformers : Library for loading and fine-tuning foundational and multimodal models.
8] LangChain + LlamaIndex : Used for orchestration and RAG-style workflows with LCMs.
- Internet & Web Basics
- HTML, CSS, JavaScript Fundamentals
- Client-Server Model
- APIs and HTTP Methods
- Frontend vs Backend
- Databases in Web Development
- Git & GitHub (Commit, Branch, Merge, Pull Requests)
- VS Code / IDE Setup Guides
- Command Line Basics
- Docker Basics
- Cloud Computing Overview (AWS, Azure, GCP)
Contributions are welcome! If you have useful notes, examples, or explanations to enhance this repository, feel free to open a pull request.
This repository is open-source and available under the MIT License.