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

Events

 

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

Replacing protobuf with Open api spec will allow to deprecate Datumgen and allow same strong type checking

Replacing with Kinesis data stream SDK in client can deprecate the need to maintain our own server.

Platform Team Names

 

DataPipeline

 

https://www.youtube.com/watch?v=kGT4PcTEPP8

AWS This Week

Derivative

 https://www.youtube.com/watch?v=wUZcIYx-7a4

How Canva uses Census and the Snowflake Marketing Data Cloud

 

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

https://www.fivetran.com/

https://www.getdbt.com/

Reverse ETL: Census specializes in reverse ETL (Extract, Transform, Load), which is the process of moving data from a data warehouse back into operational systems. This is the opposite of traditional ETL, which moves data into the warehouse. Reverse ETL ensures that the most current and relevant data is available in the tools used by different business teams.

 

OCAML

 

https://www.youtube.com/watch?v=spwvg0DThh4

https://www.youtube.com/watch?v=VsX4YuimYRI

https://www.youtube.com/watch?v=6o74Kdpy5pk

 

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