How to Build an LLM from Scratch in Python Using AI

 https://www.youtube.com/watch?v=WEeoiPavAzM

Debugging Python FastAPI Made EASY in VS Code!

 

https://www.youtube.com/watch?v=Xi0FwZuXMZQ

MCP server

 https://www.youtube.com/watch?v=jLM6n4mdRuA

CONCURRENCY

https://www.youtube.com/watch?v=3X93PnKRNUo

 

fonts

 

-- Font format

https://www.blogger.com/blog/post/edit/6820506423268523140/2128967685746882502

-- Font Terms

https://www.blogger.com/blog/post/edit/6820506423268523140/108820598277300579

--Variable fonts

https://www.blogger.com/blog/post/edit/6820506423268523140/2240018139753831198

-- fonts

https://www.blogger.com/blog/post/edit/6820506423268523140/3143000195812424525

https://www.high-logic.com/font-manager/maintype?fbclid=IwAR3NpMFXxzqxLsCRpwaPl-9O-CIvkwGg5NyMd4abD_IYlfJNPaAlFKzBhe0

https://kern-on.com

python-concurrency

 https://realpython.com/python-concurrency/

Interview

 

Coder pad

https://www.youtube.com/watch?v=x_FB7bGfgss

https://www.youtube.com/watch?v=AtPHACCeuiw

Codility
HackerRank
CoderByte
CodeSignal
TestDome
Leetcode

LLM from Scratch

 

https://towardsdatascience.com/how-to-build-an-llm-from-scratch-8c477768f1f9

https://commoncrawl.org/faq

 “I hit the baseball with a bat.” 

“I hit the bat with a baseball.” 

https://www.youtube.com/watch?v=FepOyFtYQ6I&feature=youtu.be

PyTorch for deep Learning

https://www.udemy.com/course/pytorch-for-deep-learning-with-python-bootcamp/learn/lecture/15027094#reviews

Machine Learning

https://www.udemy.com/course/machine-learning-data-science-foundations-masterclass/learn/lecture/22926432#overview

https://www.udemy.com/course/machinelearning/learn/lecture/34779566#overview

https://www.youtube.com/watch?v=E0Hmnixke2g

 

https://colab.research.google.com/drive/1934DQETINwi7yt-wE3nDd4CxH8ZZNJlE?usp=sharing#scrollTo=KgXZP2TN5hFM

Multiple linear Regression

https://drive.google.com/drive/folders/1OFNnrHRZPZ3unWdErjLHod8Ibv2FfG1d

Simple Linear Regression Example

Multiple Linear Regression

Polynomial Regression

Multiple Linear Regression Fit

Reinforcement Learning

https://www.youtube.com/watch?v=lfPEJPHUllg&list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&index=1

Machine Learning and AI

Maths

https://sriramal.blogspot.com/2021/01/mathematics.html

https://sriramal.blogspot.com/2022/10/linear-algebra.html

https://sriramal.blogspot.com/2022/12/algebra.html

https://sriramal.blogspot.com/2022/12/the-main-ideas-of-least-squares-and.html

https://sriramal.blogspot.com/2022/12/algebraic-curves.html

https://sriramal.blogspot.com/2023/01/calculus.html

https://sriramal.blogspot.com/2023/02/derivatives.html

https://sriramal.blogspot.com/2023/02/applications-of-trig-functions.html

https://sriramal.blogspot.com/2023/02/the-essence-of-calculus.html

Machine Learning

https://sriramal.blogspot.com/2018/07/machine-learning-part-1.html

https://sriramal.blogspot.com/2019/07/machine-learning_28.html

https://sriramal.blogspot.com/2019/07/machine-learning.html

https://sriramal.blogspot.com/2019/07/machine-learning_90.html

https://sriramal.blogspot.com/2019/07/machine-learning_66.html

https://sriramal.blogspot.com/2019/08/mathematical-foundation-for-machine.html

https://sriramal.blogspot.com/2019/08/machine-learning.html

https://sriramal.blogspot.com/2019/08/data-analytics-and-machine-learning.html

https://sriramal.blogspot.com/2019/08/2019-aws-sagemaker-and-machine-learning.html

https://sriramal.blogspot.com/2019/09/data-science.html

https://sriramal.blogspot.com/2019/09/data-engineer-vs-data-scientist.html

https://sriramal.blogspot.com/2019/11/data-science.html

https://sriramal.blogspot.com/2019/11/artificial-intelligence.html

https://sriramal.blogspot.com/2019/11/machine-learning.html

https://sriramal.blogspot.com/2020/01/machine-learning-interview.html

https://sriramal.blogspot.com/2020/11/machine-learning.html

https://sriramal.blogspot.com/2020/11/machine-learning_21.html

https://sriramal.blogspot.com/2022/11/data-science-foundations-fundamentals.html

https://sriramal.blogspot.com/2022/11/the-main-ideas-of-least-squares-and.html

https://sriramal.blogspot.com/2023/01/confusion-matrix.html

https://sriramal.blogspot.com/2023/01/linear-regression-vs-logistic-regression.html

https://sriramal.blogspot.com/2023/02/httpswww.html

https://sriramal.blogspot.com/2023/02/bayes-theorem-intuition.html

https://sriramal.blogspot.com/2023/02/first-machine-learning-solution.html

https://sriramal.blogspot.com/2023/02/machine-learning-models-in-python-with.html

https://sriramal.blogspot.com/2023/02/machine-learning.html

https://sriramal.blogspot.com/2023/02/2020-machine-learning-roadmap.html

https://sriramal.blogspot.com/2023/02/regression-vs-classification.html

https://sriramal.blogspot.com/2023/02/machine-learning-python.html

AI

https://sriramal.blogspot.com/2021/01/image-classification-with-pytorch.html

https://sriramal.blogspot.com/2022/11/learn-pytorch-for-deep-learning-in-day.html

https://sriramal.blogspot.com/2022/11/neural-networks.html

https://sriramal.blogspot.com/2022/11/cnn-convolutional-neural-networks.html

https://sriramal.blogspot.com/2022/11/practical-deep-learning-for-coders-2022.html

https://sriramal.blogspot.com/2023/01/deep-learning-image-recognition.html

https://sriramal.blogspot.com/2023/03/clip.html

Latex

 

https://detexify.kirelabs.org/classify.html

https://www.youtube.com/watch?v=lgiCpA4zzGU

async

 

https://www.youtube.com/watch?v=r_clm92NI1s

https://www.youtube.com/watch?v=9j1dZwFEJ-c

LLM from scratch

 https://lightning.ai/

LLM from Scratch

https://www.youtube.com/watch?v=kCc8FmEb1nY&t=14s

source code and LLM

 

https://sourcegraph.com/blog/how-cody-understands-your-codebase

For Code

CodeLLaMA

StarCoder

GPT4All-J (Code-Focused Models)

SantaCoder

PrivateGPT for Code

AnythingLLM

Chatd

GPT4All

PrivateGPT

LocalAI

Decodable vs AWS Managed Service , Link

 

https://www.youtube.com/watch?v=zfK3mJ_bkSc

https://www.youtube.com/watch?v=jhJQp46QB_c

Attention is all you need (Transformer)

 

https://www.youtube.com/watch?v=bCz4OMemCcA

Neural Network Types

 

Neural Network Type	Data Type	Primary Use Cases	Key Feature
Feedforward (FNN)	Structured/tabular data	Basic classification/regression	Simple forward flow
Convolutional (CNN)	Grid-like data (images, videos)	Image classification, object detection	Convolutional layers for feature extraction
Recurrent (RNN)	Sequential data	Time-series forecasting, NLP, speech recognition	Sequential processing with memory
Graph (GNN)	Graph-structured data	Social networks, molecule analysis	Message passing between nodes
Autoencoders	Structured/tabular data	Data compression, anomaly detection	Encoding and decoding
Generative Adversarial	Images, text	Image generation, text-to-image	Generator and discriminator networks
Transformer	Sequential (NLP, images)	Language translation, text generation	Attention mechanism for sequence processing

https://www.youtube.com/watch?v=OFbnpY_k7js

https://www.youtube.com/watch?v=SV2huMdNs9c

Graph Neural Network - GNN

 

https://www.youtube.com/watch?v=K9w7-4N_ZCI

Intro Video: https://www.youtube.com/watch?v=xFMhLp52qKI

 

GraphSage example: https://medium.com/analytics-vidhya/ohmygraphs-graphsage-in-pyg-598b5ec77e7b

PinSage Paper Walkthrough: https://youtu.be/ed0NJdqwEyg?si=3GV6jIGzdd760W5i

HetGNN Paper Walkthrough: https://www.youtube.com/watch?v=GMDlfJT0nPc

Apache Flink

 

https://www.youtube.com/watch?v=fYO5-6Owt0w

https://www.youtube.com/watch?v=FoypLT2W91c

https://www.youtube.com/watch?v=xu3A_7DcRgQ

https://www.canva.dev/blog/engineering/product-analytics-event-collection/

Delivery Stream is focused on delivering data to various storage or analytics destinations with optional data transformation, ideal for near real-time ETL (Extract, Transform, Load) processes.

Data Stream is more about ingesting and processing real-time data at low latency, where you can build custom applications that consume and process the data in real time.

https://aws.amazon.com/solutions/implementations/streaming-data-solution-for-amazon-kinesis/

https://www.youtube.com/watch?v=-WFhxvX_XYw

https://www.youtube.com/watch?v=K2ibvfmFh8Y

https://www.youtube.com/watch?v=9YLOA-8UijI

Framework	Language(s) Supported	Strengths	Use Cases
Apache Kafka Streams	Java, Scala	Tight integration with Kafka, lightweight	Real-time analytics, microservices, event-driven architectures
Apache Spark Streaming	Java, Scala, Python, R	Unified API for batch/stream, scalability	Continuous ETL, real-time data processing, streaming ML
Apache Storm	Java, Python, Ruby, Clojure	Low-latency processing	Real-time analytics, complex event processing
Apache Samza	Java, Scala	Strong state management, scalable	Real-time pipelines, monitoring
Google Cloud Dataflow	Java, Python	Managed service, auto-scaling, unified batch/stream	Cloud-native real-time analytics, ETL
Amazon Kinesis Data Streams	Java, Python, Node.js, Ruby	Managed service, AWS ecosystem integration	Real-time data ingestion, log processing
Apache NiFi	Java, XML	User-friendly, strong data lineage	Data flow automation, ETL
Azure Stream Analytics	SQL-like language	Managed service, Azure integration	IoT data processing, real-time analytics