Coding TODOS Link to heading
Board Link to heading
TODOs Link to heading
- https://pytorch.org/tutorials/
- https://www.tensorflow.org/tutorials
- https://www.tensorflow.org/guide
- Review blockchain roadmap or smthng
- https://python-course.eu/python-tutorial/
- Book: Document Violent Python
- Read Django Documentation: https://docs.djangoproject.com/en/5.0/
- Read Vuejs Documentation: https://vuejs.org/guide/introduction.html
- Django Book
- VueJS Book
- Automate the Boring Stuff with Python: Practical Programming for Total Beginners
- Tutorial Howtos: https://docs.python.org/3/howto/index.html
- Tutorial Library: https://docs.python.org/3/library/index.html
- Book: Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming
- Book: Mastering python networking, Eric chau
- Read python documentation
- Redo Bash notes
- Bash: built-in commands, pipes, filters
- https://exercism.org/tracks/python
- https://www.edx.org/learn/python/harvard-university-cs50-s-introduction-to-programming-with-python
- Book: Cracking the code interview, Gayle laakmann
- Kindle: Design Patterns: Elements of Reusable Object-Oriented Software
- PDF Cleanup at: /home/n0kt/Dropbox/Docs/Coding
Blockchain TODOs Link to heading
- Web3: web3modal, ether.js, web3.js
- Smartcontracts: nft 720/721, ERC20, 1155
- Frameworks: Solidity, Algorand, Waveportal
Some other TODOs Link to heading
- AI: supervised/unsupervised, reiforcenement algo. Regresion, classification, clustering, domensionality reduction, decision trees, random forest, neural networks, npl
- Datascience: data preprocessing, visualization, statistical analysis, probagbility, model evaluation, time series analysis, anomaly detection
- https://exercism.org/tracks/javascript
- https://exercism.org/tracks/php
- Book: Django Book
- Book: VueJS book
- Book: You don’t know JS, kyle simpsons
- Book: Javascript: es6 features, modules, promises, async/await
- Book: JavaScript the definitive guide, Flanagan
- Javascript algorithms: https://github.com/trekhleb/javascript-algorithms
- Find more Django Rest Framework learning resources
- Find more Vuejs learning resources
- https://developers.google.com/machine-learning/crash-course
- https://www.coursera.org/specializations/deep-learning?irclickid=UtrQdoxHhxyPTtYToZ0KaX9cUkF2lFUW-U3tXE0&irgwc=1
- https://www.coursera.org/learn/machine-learning-with-python?irclickid=UtrQdoxHhxyPTtYToZ0KaX9cUkF2lFUW-U3tXE0&irgwc=1
- https://exercism.org/tracks/java
- Book: Find a good book for springboot
- https://exercism.org/tracks/rust
- https://exercism.org/tracks/go
- Learn Frontend Web development again (frameworks list): reactjs, tailwind
- Learn Backend Web development again (frameworks list): laravel
- Hands-on: https://exercism.org/tracks/typescript
- https://leetcode.com/explore/
- Critical thinking on programming
- Python: numpy, pandas, matploitlib, tensorflow, keras, flask, Django
- Learn typescript
- Learn Perl? is it worth? regex, data manipulation?
- Go: concurrency, goroutines, channels, interfaces
- CPP: STL, containers, algorithms, iterators, memory management, pointers
- Algos: linked lists, trees, graphs, sorting algos, search algos, shortest path algos, dynamic programming, hashing, bloom filters
Books on LLMS Link to heading
- Practical Natural Language Processing by Sowmya Vajjala
- Natural Language Processing with Transformers by Lewis Tunstall
- Transformers for natural language processing by Denis Rothman
- GPT-3 Building innovative nlp products using large language models
- Course: HugginFace Transformers Course
- Course: ChatGPT and Large Language Models A Practical Guide
- Course: CS224n: Deep Learning for NLP by Stanford University
- Course: CS324: Large Language Models by Stanford University
Books on DeepLearning Link to heading
- Book: hands-on machine learning with scikit-learn, keras, & tensorflow
- Book: Deep learning with python, francois Chollet
- Book: AI And machine learning for coders by Laurecen Moroney
- Book: Machine learning with pytorch and scikit-learn
- Book: AI A very short introduction
- Book: Practical linear algebra a geomtry toolbox
- Book: introduction to machine learning with python
- Book: The elements of statistical learning
- Book: deep learning by ian goodfellow
- Read arXiv papers
- Course: Khan academy for math
- Course: Codecademy for python
- Course: Coursera Machine learning by Andrew Ng
- Course: Fast.ai Deep learning course
- Course: Coursera NLP Specialization
📖 Books to Read Link to heading
- Frontend development projects Vuejs, Raymond Camden
- Introductions to algorithms, cormen
- Mastering Blockchain, Imran
- Fundamentals of software architecture, Richards ford
- Learning sql, beaulieu
- Web development with Django, Ben shaw
- Mastering php7, branko ajzele
- Effective java, Bloch
- Laravel up and running, Stauffer
- Deep learning for coders fastai pytorch, Howard gugger
- Machine learning design patterns, lakshmanan
- Becoming a data head, Gutman goldmeier
- Hands-on machine learning scikit-learn keras tensor flow
- Deep Learning, Ian good fellow
- Linear algebra and applications, lay macdonald
- Algebra Baldor
- Estadística, Spiegel
- 1000 problemas de antonov artimetica, algebra, geo…
- Algebra de Baldor solucionario, amador lopez
Books to read (kindle) Link to heading
- Head First Design Patterns: Building Extensible and Maintainable Object-Oriented Software
- Mythical Man-Month, Anniversary Edition, The: Essays On Software Engineering
- The Pragmatic Programmer: your journey to mastery, 20th Anniversary Edition
- Data Structures and Algorithms Made Easy: Data Structures and Algorithmic Puzzles
- Software Architecture in Practice: Software Architect Practice_c3 (SEI Series in Software Engineering)
- Code Complete (Developer Best Practices)
- Top-Down Network Design (Networking Technology)
- SQL Injection Attacks and Defense
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6
- Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
- Deep Learning (Adaptive Computation and Machine Learning series)
- C Programming Language: C PROGRAMMING LANG _p2
- The C++ Programming Language: The C++ Programm Lang_p4
- C++ Primer
- Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin Series)
- The Complete Software Developer’s Career Guide: How to Learn Your Next Programming Language, Ace Your Programming Interview, and Land The Coding Job Of Your Dreams
Books to search Link to heading
- Mathematics for Machine Learning" by Marc Peter Deisenroth
- Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron
- The Hundred-Page Machine Learning Book" by Andriy Burkov
- Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili
- attern Recognition and Machine Learning" by Christopher Bishop
- Machine Learning: A Probabilistic Perspective" by Kevin Murphy
- Deep Learning" by Yoshua Bengio, Ian Goodfellow, and Aaron Courville
- Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz and Shai Ben-David
- Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
- Convex Optimization" by Stephen Boyd and Lieven Vandenberghe
- Deep Learning for Natural Language Processing" by Palash Goyal, Sumit Pandey, and Karan Jain
- Deep Learning with Python" by François Chollet
- Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play" by David Foster
- Clean code, robert Martin
- Design patterns, erich gamma
- Refactoring, martin fowler
- The pragmatic programmer, david thomas
- Code complete, Steve McConnel
- Domain-driven design, eric evans
- The clean coder, robert martin
- Building microservices, sam newman
- Software architecture in practice, len bass
- deep learning, ian goodfellow
- Hands-on machine learning with scikit-lean, aurelien geron
- The hundred-page machine learning book, andriy burkov
- Patter recognition and machine learning, christopher bishop
- Python for data analysis, wes mckinney
- The elements of statistical learning, trevor hastie
- Data science for business, foster provost
- Machine learning, kevin murphy
- Practical statistics for data scientsits, peter bruce
- Reinforcement learning, richard sutton
Resources Link to heading
- Design patterns: https://refactoring.guru/design-patterns/catalog
- Math resources
- Papers: https://arxiv.org/
Python resources Link to heading
- https://pythonprinciples.com/
- https://automatetheboringstuff.com/
- Python language reference: https://docs.python.org/3/library/index.html
- Python standard library: https://docs.python.org/3/library/index.html
- Python Cheatsheet: https://www.pythoncheatsheet.org/
- Python crush course repo: https://github.com/ehmatthes/pcc_2e/
Resources on algorithms Link to heading
- C++ algo: https://github.com/TheAlgorithms/C-Plus-Plus
- Python algorithms: https://github.com/TheAlgorithms/Python
- Data structures: https://code.tutsplus.com/series/data-structures-succinctly-part-1--cms-551
- Data Structures and algorithms specialization: https://www.coursera.org/specializations/data-structures-algorithms
- Data structure and algorithms: https://www.tutorialspoint.com/data_structures_algorithms/
- Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1
- Data structures Geeksforgeeks: https://www.geeksforgeeks.org/data-structures/
- Visualize algos: https://visualgo.net/en
- Introduction to algorithms by Cormen: https://www.amazon.co.uk/Introduction-Algorithms-Thomas-H-Cormen/dp/0262533057
- Algorithms by Kevin Wayne: https://www.amazon.co.uk/Algorithms-Robert-Sedgewick/dp/032157351X
- Learn to program: https://pine.fm/LearnToProgram/
- An awesome list for competitive programming! - https://codeforces.com/blog/entry/23054
Videos on Datascience Link to heading
- Python for Data Science and Machine Learning Bootcamp: https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/
- Dataquest: https://www.dataquest.io/blog
- Practical SQL Bootcamp for Data Analysts and Data Scientists: https://www.udemy.com/course/practical-sql-bootcamp-for-analysts/
- Datascience with python: https://jakevdp.github.io/PythonDataScienceHandbook/
- awesome-datascience: https://github.com/academic/awesome-datascience
- Data Science Specialization: https://www.coursera.org/specializations/jhu-data-science
- Python for data analysis: https://www.amazon.com/gp/product/1491957662
- Data scientist roadmap: https://www.geeksforgeeks.org/how-to-become-data-scientist-a-complete-roadmap/
- Data engineer roadmap: https://towardsdatascience.com/data-science-learning-roadmap-for-2021-84f2ba09a44f
- Big Data Hadoop and Spark Developer Training Course - https://dooey.org/product/big-data-hadoop-and-spark-developer/
- https://www.udemy.com/course/the-complete-hands-on-course-to-master-apache-airflow/
- https://www.udemy.com/course/snowflake-essentials/
- https://www.udemy.com/course/spark-and-python-for-big-data-with-pyspark
Resources on Machine learning Link to heading
- Deep learning roadmap: https://www.mltut.com/deep-learning-roadmap/
- http://www.crm.umontreal.ca/2017/MAIN2017/pdf/Vincent_MAIN_2017.pdf
- https://medium.com/analytics-vidhya/deep-learning-101-the-theory-c23a95d1d5cd
- Deep Learning (Adaptive Computation and Machine Learning series): https://www.amazon.com/-/Ian-Goodfellow-ebook-dp-B08FH8Y533/dp/B08FH8Y533/
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: https://www.amazon.com/-/es/Aur-C3-A9lien-G-C3-A9ron-ebook-dp-B07XGF2G87/dp/B07XGF2G87/
- https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/
- Machine Learning Specialization - https://www.coursera.org/specializations/machine-learning-introduction
- Deep learning specialization: https://www.coursera.org/specializations/deep-learning
- https://github.com/owainlewis/awesome-artificial-intelligence
- https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/
- Natural Language Processing: https://www.coursera.org/learn/language-processing
- Deep learning in computer vision: https://www.coursera.org/learn/deep-learning-in-computer-vision
- Machine learning with python by IBM: https://www.edx.org/course/machine-learning-with-python-a-practical-introduct
- Machine learning crash course by Google: https://developers.google.com/machine-learning/crash-course
- Machine Learning McGraw-Hill: https://www.amazon.com/gp/product/0071154671
- Artificial Intelligence A-Z: https://www.udemy.com/course/artificial-intelligence-az
- AI by Berkley: http://ai.berkeley.edu/lecture_videos.html?ref=hackernoon.com
- neural networks and deep learning: http://neuralnetworksanddeeplearning.com/
- Probabilistic Machine Learning: https://probml.github.io/pml-book/
- Common ML algorithms: https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/
- Learning path to ML: https://www.analyticsvidhya.com/learning-path-learn-machine-learning/
Coding Reading Link to heading
├── Andrei Alexandrescu - Modern C++ Design.pdf
├── Andrew Hunt & David Thomas - Pragmatic Programmer.pdf
├── Antony Polukhin - Boost C++ Application Development Cookbook.pdf
├── bash
│ ├── abs-guide.pdf
│ └── Bash-Beginners-Guide.pdf
├── Beej - Guide to Network Programming.pdf
├── Ben Lynn - Git Magic.pdf
├── Bjarne Stroustrup - Programming Principles and Practice Using C++.pdf
├── Bjarne Stroustrup - The C++ Programming Language 4th Edition - 2013.pdf
├── Bjarne Stroustrup - The C++ Programming Language Special Edition.PDF
├── Bruce Eckel - Thinking in Java 4th Edition.pdf
├── c
│ ├── Linden_-_Expert_C_Programming-_Deep_C_Secrets.pdf
│ ├── lkmpg.pdf
│ └── the_c_programming_language_2.pdf
├── c_cpp
│ ├── C++ A Beginners Guide 2nd Edition (2003).pdf
│ ├── C++.pdf
│ ├── cprogramming_tutorial.pdf
│ ├── ctext.pdf
│ ├── DS286.AUG2016.Lab2_.cpp_tutorial.pdf
│ ├── Learning_C_C++_Step-By-Step.pdf
│ ├── thecbook.pdf
│ └── tutorial.pdf
├── Chad Fowler - The Passionate Programmer, 2nd edition.pdf
├── Charles Petzold - Programming Windows (5th Edition, WinAPI).pdf
├── Charles Petzold - Programming Windows (6th Edition, Win8).pdf
├── Eldad Eilam - Reversing - Secrets of Reverse Engineering.pdf
├── Gang of Four - Design Patterns - Elements of Reusable Object-Oriented Software.pdf
├── Jack Crenshaw - Lets Build a Compiler.pdf
├── Jasmin Blanchette, Mark Summerfield - GUI Programming with QT 4 (2nd Edition).pdf
├── java
│ ├── java-a-beginners-guide-1720064.pdf
│ ├── java_tutorial.pdf
│ ├── LearnJava.pdf
│ ├── o reilly_-_learning_java.pdf
│ ├── SpringByExample.pdf
│ └── spring_tutorial.pdf
├── javascript
│ ├── [O`Reilly] - JavaScript. The Definitive Guide, 6th ed. - [Flanagan].pdf
│ └── Oreilly.JavaScript.The.Definitive.Guide.6th.Edition.Apr.2011.pdf
├── Jesse Schell - The Art of Game Design A Book of Lenses .pdf
├── JS package distribution.pdf
├── Katie Salen & Eric Zimmerman - Rules of Play Game Design Fundamentals.pdf
├── Kernighan, Ritchie - The C Programming Language, 2nd edition.pdf
├── Kevin D. Mitnick & William L. Simon - The Art Of Deception.pdf
├── Kip R. Irvine - Assembly Language for x86 Processors, 6th edition.pdf
├── Mark Lutz - Learning Python, 5th Edition - 2013.pdf
├── Mark Lutz - Programming Python, Fourth Edition - 2010.pdf
├── Mark Russinovich - Windows Internals, P1.pdf
├── Mark Russinovich - Windows Internals, P2.pdf
├── Mark Summerfield - Rapid GUI Programming with Python and Qt.pdf
├── Martin Fowler - Patterns of Enterprise Application Architecture (2002).pdf
├── Martin Fowler - Refactoring - Improving the Design of Existing.pdf
├── modern-cpp-tutorial-en-us.pdf
├── perl
│ ├── 30728.pdf
│ ├── (ebook pdf) Teach Yourself Perl in 21 Days.pdf
│ ├── learning_perl.pdf
│ ├── Learning Perl.pdf
│ ├── Oreilly - Learning Perl 6th Edition Jun 2011.pdf
│ ├── perl-for-beginners.pdf
│ ├── perl.pdf
│ └── perl_tutorial.pdf
├── php
│ └── PHP_by_Example.pdf
├── programming-books
│ ├── Andrei Alexandrescu - Modern C++ Design.pdf
│ ├── Andrew Hunt & David Thomas - Pragmatic Programmer.pdf
│ ├── Antony Polukhin - Boost C++ Application Development Cookbook.pdf
│ ├── Beej - Guide to Network Programming.pdf
│ ├── Ben Lynn - Git Magic.pdf
│ ├── Bjarne Stroustrup - Programming Principles and Practice Using C++.pdf
│ ├── Bjarne Stroustrup - The C++ Programming Language 4th Edition - 2013.pdf
│ ├── Bjarne Stroustrup - The C++ Programming Language Special Edition.PDF
│ ├── Bruce Eckel - Thinking in Java 4th Edition.pdf
│ ├── Chad Fowler - The Passionate Programmer, 2nd edition.pdf
│ ├── Charles Petzold - Programming Windows (5th Edition, WinAPI).pdf
│ ├── Charles Petzold - Programming Windows (6th Edition, Win8).pdf
│ ├── Eldad Eilam - Reversing - Secrets of Reverse Engineering.pdf
│ ├── Gang of Four - Design Patterns - Elements of Reusable Object-Oriented Software.pdf
│ ├── Jack Crenshaw - Let s Build a Compiler.pdf
│ ├── Jasmin Blanchette, Mark Summerfield - GUI Programming with QT 4 (2nd Edition).pdf
│ ├── Jesse Schell - The Art of Game Design A Book of Lenses .pdf
│ ├── Katie Salen & Eric Zimmerman - Rules of Play Game Design Fundamentals.pdf
│ ├── Kernighan, Ritchie - The C Programming Language, 2nd edition.pdf
│ ├── Kevin D. Mitnick & William L. Simon - The Art Of Deception.pdf
│ ├── Kip R. Irvine - Assembly Language for x86 Processors, 6th edition.pdf
│ ├── Mark Lutz - Learning Python, 5th Edition - 2013.pdf
│ ├── Mark Lutz - Programming Python, Fourth Edition - 2010.pdf
│ ├── Mark Russinovich - Windows Internals, P1.pdf
│ ├── Mark Russinovich - Windows Internals, P2.pdf
│ ├── Mark Summerfield - Rapid GUI Programming with Python and Qt.pdf
│ ├── Martin Fowler - Patterns of Enterprise Application Architecture (2002).pdf
│ ├── Martin Fowler - Refactoring - Improving the Design of Existing.pdf
│ ├── Randall Hyde - The Art of Assembly Language, 2nd Edition.pdf
│ ├── Raph Koster - A Theory of Fun for Game Design.pdf
│ ├── Robert Martin - Clean Code.pdf
│ ├── Robert Sedgewick, Philippe Flajolet - An Introduction to the Analysis of Algorithms, 2nd Edition.pdf
│ ├── Scott Chacon - Pro Git.pdf
│ ├── Scott Meyers - Effective C++ 3rd Edition.pdf
│ ├── Scott Meyers - Effective Modern C++.pdf
│ ├── Scott Meyers - Effective STL.pdf
│ ├── Scott Meyers - More Effective C++.pdf
│ ├── Scott Rogers - Level Up! The Guide to Great Video Game Design.pdf
│ ├── Steve McConnell - Code Complete (2nd edition).pdf
│ ├── Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein - Introduction to algorithms, 3rd edition.pdf
│ └── Tim Ottinger - Vim Like A Pro.pdf
├── python
│ ├── gray-hat-pyton.pdf
│ ├── Learning Python, 5th Edition.pdf
│ ├── Learn Python The Hard Way, 3rd Edition .pdf
│ ├── LPTHW.pdf
│ ├── py.pdf
│ └── python_for_hackers.pdf
├── Randall Hyde - The Art of Assembly Language, 2nd Edition.pdf
├── Raph Koster - A Theory of Fun for Game Design.pdf
├── Robert Martin - Clean Code.pdf
├── Robert Sedgewick, Philippe Flajolet - An Introduction to the Analysis of Algorithms, 2nd Edition.pdf
├── ruby
│ ├── jmif3s8lar4uaf67j2ni.pdf
│ ├── LearnRubyTheHardWay.pdf
│ └── poignant-guide.pdf
├── Scott Chacon - Pro Git.pdf
├── Scott Meyers - Effective C++ 3rd Edition.pdf
├── Scott Meyers - Effective Modern C++.pdf
├── Scott Meyers - Effective STL.pdf
├── Scott Meyers - More Effective C++.pdf
├── Scott Rogers - Level Up! The Guide to Great Video Game Design.pdf
├── sql
│ ├── Structured Query Language.pdf
│ └── teach_urself_sql.pdf
├── Steve McConnell - Code Complete (2nd edition).pdf
├── Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein - Introduction to algorithms, 3rd edition.pdf
└── Tim Ottinger - Vim Like A Pro.pdf
Blockchain Resources Link to heading
- https://dooey.org/product/blockchain-developer/
- Blockchain A-Z™: Learn How To Build Your First Blockchain: https://www.udemy.com/course/build-your-blockchain-az/
- https://www.youtube.com/watch?v=G4uR_Fl75uk
- https://www.youtube.com/watch?v=NjcuJmAB0Mo
- https://www.youtube.com/watch?v=JzHZX4Pq5eY
- https://www.youtube.com/watch?v=28XhilndyVI
- Dapp-learning: https://github.com/Dapp-Learning-DAO/Dapp-Learning
- Sandwich attacks: https://github.com/libevm/subway
- https://www.useweb3.xyz/
Coding videos Link to heading
https://www.youtube.com/watch?v=Ad0DKl7HUwI&list=WL&index=178 https://www.youtube.com/watch?v=grnP3mduZkM&list=WL&index=177 https://www.youtube.com/watch?v=2qJ1wrLcgx0&list=WL&index=157 https://www.youtube.com/watch?v=VXrGp5er1ZE&list=WL&index=144 https://www.youtube.com/watch?v=1uTAfHqAi0U&list=WL&index=126 https://www.youtube.com/watch?v=HtSuA80QTyo&list=WL&index=124 https://www.youtube.com/watch?v=X393OZqSPUk&list=WL&index=89
Contribute projects Link to heading
Go lang:
- https://github.com/helm/helm
- https://github.com/mozilla/sops
- https://github.com/hashicorp/terraform-provider-aws
- https://github.com/hashicorp/terraform
- https://github.com/kubernetes/kubernetes
- https://github.com/traefik/traefik
- https://github.com/docker-slim/docker-slim
- https://github.com/prometheus/prometheus
- https://github.com/hashicorp/consul
- https://github.com/istio/istio
- https://github.com/tenable/terrascan
- https://github.com/gruntwork-io/terratest
- https://github.com/jesseduffield/lazygit
- https://github.com/junegunn/fzf
- https://github.com/GoogleCloudPlatform/terraformer
Python: