Step by step guide to becoming an Android developer in 2024
+
### Tools
diff --git a/Compiler Design/readme.md b/Compiler Design/readme.md
new file mode 100644
index 00000000..2cf0942b
--- /dev/null
+++ b/Compiler Design/readme.md
@@ -0,0 +1,250 @@
+# Compiler Design
+
+Compiler design involves the creation of programs that translate high-level code written by programmers into machine code that can be executed by a computer's hardware. This field is fundamental in computer science and involves several stages including lexical analysis, syntax analysis, semantic analysis, optimization, and code generation. The study and implementation of compilers help in understanding the intricacies of programming languages and the efficient execution of programs.
+
+## Table of Contents
+
+- [Roadmap](#Roadmap)
+- [Courses](courses)
+- [Free Cources](#free-courses)
+- [Paid Courses](#paid-courses)
+- [Books](#books)
+- [YouTube](#youtube)
+ - [Youtube Channels](#youtube-channels)
+ - [YouTube Playlist](#youtube-playlists)
+- [Interview](#interview)
+- [GitHub Repositories](#github-repositories)
+- [Communities](#communities)
+ - [Reddit Communities](#reddit-communities)
+ - [Other Communities](#other-communities)
+- [Conclusion](#conclusion)
+- [Projects](#projects)
+
+## Roadmap
+
+To become proficient in compiler design, one needs a thorough understanding of various aspects of compilers. Here’s a roadmap to help guide your learning journey.
+
+
A step-by-step guide to writing a simple C interpreter, useful for understanding the basics of compiler design.
+
+
+
+## Communities
+
+Join these communities to learn from others, share your knowledge, and stay updated with the latest trends in compiler design.
+
+### Reddit Communities
+
+
A Discord server with channels dedicated to various aspects of programming, including compiler design.
+
+
+
+## Conclusion
+
+Mastering compiler design requires a combination of theoretical knowledge and practical experience. Utilize the resources provided to build a strong foundation and stay engaged with the community to keep up with advancements in the field.
+
+## Projects
+
+Apply your knowledge by working on these projects to gain hands-on experience.
+
+
Built a project to understand the working basics of compiling for academic purposes, where I customized a programming language that can be compiled using Flex and Bison.
A simple compiler for a custom language, implemented to illustrate compiler design concepts.
+
+
+
+By following this guide, you'll be well-equipped to dive into the world of compiler design, from learning the fundamentals to applying your skills in practical projects and participating in the community.
diff --git a/Computer Architecture/README.md b/Computer Architecture/README.md
index 77067d78..f3ecd471 100644
--- a/Computer Architecture/README.md
+++ b/Computer Architecture/README.md
@@ -137,6 +137,14 @@ Computer Architecture is the study of the structure, organization, and design of
This book provides an in-depth understanding of superscalar processor design.
### YouTube Channels
-> These YouTube channels offer valuable content on Computer Architecture:
+> Below are some YouTube channels offering educational content on Computer Architecture:
@@ -203,17 +227,19 @@ Computer Architecture is the study of the structure, organization, and design of
Provides in-depth lectures on Computer Architecture by Princeton University professor David Wentzlaff.
+
+
### YouTube Playlists
-> These YouTube playlists offer structured series of lectures and tutorials on Computer Architecture:
+> Below are some YouTube playlists offering educational content on Computer Architecture:
@@ -221,24 +247,26 @@ Computer Architecture is the study of the structure, organization, and design of
An MIT OpenCourseWare offering on the subject of computer system architecture.
-
### Resources for Finding Research Papers
-> These platforms provide access to research papers and articles on Computer Architecture:
+> Below are some resources for finding research papers related to Computer Architecture:
@@ -247,17 +275,21 @@ Computer Architecture is the study of the structure, organization, and design of
A freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats.
+
A freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines.
A free, AI-powered research tool for scientific literature, based at the Allen Institute for AI.
### Competitions
-> Participating in these competitions can enhance your practical skills in Computer Architecture:
+> Below are some competitions that you can consider for honing your skills in Computer Architecture:
@@ -265,18 +297,22 @@ Computer Architecture is the study of the structure, organization, and design of
The Competition on Computer Architecture Research with RISC-V focuses on innovations and advancements in the RISC-V architecture.
### Communities
-> Joining these communities can provide support, networking opportunities, and additional resources:
+> Below are some communities that you can join to learn more about Computer Architecture:
@@ -284,18 +320,22 @@ Computer Architecture is the study of the structure, organization, and design of
A social networking site for scientists and researchers to share papers and find collaborators in the field of computer architecture.
### GitHub Repositories
-> These GitHub repositories offer valuable resources and projects on Computer Architecture:
+> Below are some GitHub repositories containing resources and projects related to Computer Architecture:
@@ -303,18 +343,22 @@ Computer Architecture is the study of the structure, organization, and design of
A framework to organize and reproduce research projects, including those related to computer architecture.
### Projects
-> These project ideas can help you apply your knowledge of Computer Architecture:
+> Below are some projects that you can work on to improve your skills in Computer Architecture:
#### Beginner Projects
@@ -324,12 +368,16 @@ Computer Architecture is the study of the structure, organization, and design of
A project that involves building a dynamic binary translator using the PyDGin framework.
-
### Conclusion
diff --git a/Data Structures and Algorithm (DSA)/readme.md b/Data Structures and Algorithm (DSA)/readme.md
index e78e3496..5494c07a 100644
--- a/Data Structures and Algorithm (DSA)/readme.md
+++ b/Data Structures and Algorithm (DSA)/readme.md
@@ -57,6 +57,10 @@ Let's make coding fun and exciting! 🚀.
This DSA roadmap will provide a structured approach to mastering data structures and algorithms, covering essential concepts, practical coding exercises, and optimization techniques to enhance problem-solving skills and coding efficiency.
This roadmap provides steps from start to end for Deep Learning .
+
+
+
+### Tutorials or Courses
+
+> Discover a collection of tutorials and courses for learning the Mathamatics,Fundamentals,Algorithms and more which are requied for Deep learning.
+
+#### Fundamentals of Programming Language
+
+
+ This course is provied by the Geeks for Geeks and is perfect for both beginners and coding enthusiasts and covers essential Python fundamentals, including Object-Oriented Programming (OOPs), data structures, and Python libraries.
+ This 12 hrs video provided Freecodecamp give you the fundamental knowledge required for the data science using python including the introduction of pandas,numpy and matplotlib
+ This video by intellipaat will gives you clear understanding for the visualization of data using python,This video is suitable for both beginners and a intermediate level programmer as well.
+ This video by Freecodecamp is a good introduction to SQL (Structured Query Language), covering essential concepts and commands used in database management. It explains the basics of creating, reading, updating, and deleting data within a database.
+
+ This course is provied by the Geeks for Geeks and is perfect for both beginners and coding enthusiasts and covers essential Python fundamentals, including Object-Oriented Programming (OOPs), data structures, and Python libraries.
+ The Real Python article on Jupyter Notebooks provides an in-depth introduction to using Jupyter Notebooks for data science, Python programming, and interactive computing. The tutorial covers the basics of setting up and running Jupyter Notebooks, including how to install Jupyter via Anaconda or pip, and how to launch and navigate the notebook interface.
+ The Google Colab introductory notebook provides a comprehensive guide on how to use Google Colab for interactive Python programming. It covers the basics of creating and running code cells, integrating with Google Drive for storage, and using Colab's powerful computing resources.
+ This course is provied by the Geeks for Geeks and is perfect for both beginners and coding enthusiasts and covers essential Python fundamentals, including Object-Oriented Programming (OOPs), data structures, and Python libraries.
+ The W3Schools Pandas tutorial offers a good introduction to the Pandas library, a powerful tool for data analysis and manipulation in Python. The tutorial covers a wide range of topics, including how to install Pandas, basic operations like creating and manipulating DataFrames and Series, and more
+ The Matplotlib documentation site provides a comprehensive guide to using the pyplot module, which is a part of the Matplotlib library used for creating static, animated, and interactive visualizations in Python.
The TensorFlow Tutorials page offers a variety of tutorials designed to help users learn and apply machine learning with TensorFlow. It includes beginner-friendly guides using the Keras API, advanced tutorials on custom training, distributed training, and specialized applications such as computer vision, natural language processing, and reinforcement learning.
The PyTorch tutorials website provides a comprehensive set of resources for learning and using PyTorch, a popular open-source machine learning library. The tutorials are designed for users at various skill levels, from beginners to advanced practitioners, and cover a wide range of topics
That documentation is a great resource for anyone looking to get started with Keras, a popular deep learning framework. Keras provides a user-friendly interface for building and training deep learning models. Whether you're a beginner or an experienced practitioner, Keras offers a lot of flexibility and ease of use.
This documentation is the best for learning Scikit-learn. Scikit-learn is another fantastic library, primarily used for machine learning tasks such as classification, regression, clustering, and more. Its simple and efficient tools make it accessible to both beginners and experts in the field.
Seaborn is an amazing visualization library for statistical graphics plotting in Python. It provides beautiful default styles and color palettes to make statistical plots more attractive.
This video by Lex Fridman on "Introduction To Deep Learning" will help you understand the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers.
This video discuss the use of recurrent neural networks for modeling sequence data. We show how recurrent neural networks can be used for language modeling and image captioning, and how soft spatial attention can be incorporated into image captioning models.
These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. It focuses on medical diagnosis, medical prognosis and medical treatment by deeplearning.ai
Kaggle Datasets is a platform where users can explore, access, and share datasets for a wide range of topics and purposes. Kaggle is a popular community-driven platform for data science and machine learning competitions, and its Datasets section extends its offerings to provide access to a diverse collection of datasets contributed by users worldwide.
+
Papers With Code is a platform offering a diverse range of tools,datasets and resources for researchers and developers. These tools are designed to facilitate various aspects of research, including data analysis, machine learning, natural language processing, computer vision, and more.
+
Google Dataset Search is a tool provided by Google that allows users to search for datasets across a wide range of topics and domains. It helps researchers, data scientists, journalists, and other users discover datasets that are relevant to their interests or research needs.
+
The Deep Learning page on SuperDataScience offers a comprehensive guide to deep learning concepts and applications. It features tutorials, courses, and resources for beginners and advanced learners. The content covers neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and practical projects to apply learned skills. The site aims to help users understand and implement deep learning models effectively.
+
The iLovePhD website provides a detailed guide on how to access free IEEE datasets through the IEEE DataPort. It outlines steps to create an account, subscribe for free access during the experimental phase, search for datasets, and download them.
+
+
+
+### GitHub Repositories
+> These are some GitHub repositories you can refer to
+
+
This GitHub repository is a fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
The YouTube channel "Deeplearning.ai" hosts a variety of educational content related to artificial intelligence (AI) and machine learning (ML) created by Andrew Ng and his team at Deeplearning.ai.
The YouTube playlist titled "Complete Deep Learning" offers a series of tutorials on deep learning. The playlist includes videos that cover various deep learning concepts, techniques, and applications. It's suitable for beginners as well as those with some experience in the field. The videos are structured to provide a comprehensive learning experience, starting from basic concepts and progressing to more advanced topics.
The YouTube channel "DeepLearningAI" offers a variety of playlists focused on deep learning and AI. These playlists cover topics such as neural networks, natural language processing, machine learning fundamentals, and AI ethics. The content is designed to educate and inspire those interested in the field of artificial intelligence, ranging from beginners to advanced practitioners.
This link which navigates to geekforgeeks article focuses on Deep learning projects page on which serves as a valuable resource for individuals looking to explore, learn, and practice deep learning concepts through hands-on projects.
+
The core of the blog is a list of 16 project ideas. Each project idea includes a brief description, the skills required, and potential applications. These projects range from image recognition and natural language processing to more specialized tasks like sentiment analysis and recommendation systems.
+
The ProjectPro website offers a comprehensive collection of deep learning and deep learning projects suitable for beginners and advanced practitioners alike. The platform is designed to provide hands-on experience with real-world projects, allowing users to build practical skills in various domains of data science and big data.
+
+
+
+### Interview
+
+> These are some interview preparation resources.
+
+
This link which navigates to geekforgeeks article focuses on deep learning Interview questions
+ for both freshers and experienced individuals, ensuring thorough preparation for ML interview. This ML questions is also beneficial for individuals who are looking for a quick revision of their deep-learning concepts.
+
The Simplilearn website offers a comprehensive list of deep learning interview questions aimed at helping individuals prepare for interviews in the field of AI and deep learning. The questions cover various topics, including fundamental concepts, algorithms, techniques, and real-world applications of deep learning. The site includes both theoretical questions and practical coding problems to ensure thorough preparation.
+
+
+
+
+### Conclusion
+
+Deep learning has emerged as a powerful subset of machine learning, revolutionizing various fields including computer vision, natural language processing, and robotics. Its ability to automatically learn intricate patterns from large volumes of data has propelled advancements in AI applications, enabling tasks previously deemed impractical or impossible. However, challenges such as data efficiency, interpretability, and computational requirements remain significant. Despite these, ongoing research promises to further refine and extend deep learning's capabilities, suggesting a continued impact on technology and society in the years ahead.
diff --git a/DevOps/readme.md b/DevOps/readme.md
index 2e642404..4f26e79a 100644
--- a/DevOps/readme.md
+++ b/DevOps/readme.md
@@ -30,6 +30,7 @@ It emphasizes collaboration, automation, integration and rapid feedback cycles.
- [Books](#books)
- [GitHub Repositories](#github-repositories)
- [GitHub Projects](#github-projects)
+- [Interview](#interview)
- [Conclusion](#conclusion)
### Roadmap
@@ -1342,10 +1343,36 @@ It emphasizes collaboration, automation, integration and rapid feedback cycles.
+### Interview
+
This article consists of DevOps interview questions and answers for freshers as well as experienced candidates to get their dream job.
+
+
+
+
+
## Conclusion
DevOps is an innovative and transformative approach that bridges the gap between development and operations, fostering a culture of collaboration and continuous improvement. By integrating practices such as continuous integration, continuous deployment, and infrastructure as code, DevOps enables teams to deliver software faster and more reliably. The plethora of tools available, including Docker, Kubernetes, Jenkins, and Ansible, empowers teams to automate processes, enhance scalability, and improve efficiency. The DevOps community is vibrant and supportive, offering a wealth of resources such as tutorials, forums, and documentation to help practitioners at all levels.
To truly master DevOps, regular practice and hands-on experience are crucial—engage in coding challenges, contribute to open-source projects, and work on real-world scenarios. This practical approach not only sharpens your skills but also unlocks numerous career opportunities in the ever-evolving field of software development and IT operations.
-Never stop learning !
\ No newline at end of file
+Never stop learning !
diff --git a/UI-UX Design/readme.md b/UI-UX Design/readme.md
index a9e21f16..9059a023 100644
--- a/UI-UX Design/readme.md
+++ b/UI-UX Design/readme.md
@@ -65,11 +65,19 @@ This curated collection offers valuable UI (User Interface) and UX (User Experie
diff --git a/Web3/readme.md b/Web3/readme.md
index 2e32fecd..d3457e75 100644
--- a/Web3/readme.md
+++ b/Web3/readme.md
@@ -547,6 +547,10 @@ develop software that works with them
Step 10 - Refer to Hackathons section
Gain Practical Experience: Participate in hackathons, build projects, and contribute to open-source blockchain initiatives to apply your knowledge in real-world scenarios.
-