#MLOps #MachineLearning #DataScience #AI #ModelMonitoring #MLPipelines #Docker #MLSystemDesign #ExperimentTracking #LLMOps #NeuralNetworks #DeepLearning #AITools #MLProjects #MLOpsRoadmap
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9 machine learning concepts for ML engineers!
(explained as visually as possible)
Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
1οΈβ£ 4 strategies for Multi-GPU Training.
- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ
2οΈβ£ 4 ways to test models in production
- While testing a model in production might sound risky, ML teams do it all the time, and it isnβt that complicated.
- Implemented here: https://lnkd.in/g33mASMM
3οΈβ£ Training & inference time complexity of 10 ML algorithms
Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m
4οΈβ£ Regression & Classification Loss Functions.
- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H
5οΈβ£ Transfer Learning, Fine-tuning, Multitask Learning, and Federated Learning.
- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT
6οΈβ£ 15 Pandas to Polars to SQL to PySpark Translations.
- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND
7οΈβ£ 11 most important plots in data science
- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF
8οΈβ£ 11 types of variables in a dataset
Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p
9οΈβ£ NumPy cheat sheet for data scientists
- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE
π Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
π± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
(explained as visually as possible)
Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ
- While testing a model in production might sound risky, ML teams do it all the time, and it isnβt that complicated.
- Implemented here: https://lnkd.in/g33mASMM
Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m
- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H
- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT
- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND
- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF
Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p
- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE
#MachineLearning #DataScience #MLEngineering #DeepLearning #AI #MLOps #BigData #Python #NumPy #Pandas #Visualization
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#DataScience #HowToBecomeADataScientist #ML2025 #Python #SQL #MachineLearning #MathForDataScience #BigData #MLOps #DeepLearning #AIResearch #DataVisualization #PortfolioProjects #CloudComputing #DSCareerPathο»Ώ
βοΈ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBkπ± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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10 GitHub repos to build a career in AI engineering:
(100% free step-by-step roadmap)
1οΈβ£ ML for Beginners by Microsoft
A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.
Includes quizzes, lessons, and hands-on projects, with some videos.
GitHub repo β https://lnkd.in/dCxStbYv
2οΈβ£ AI for Beginners by Microsoft
This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.
Beginner-friendly, project-based, and full of real-world apps.
GitHub repo β https://lnkd.in/dwS5Jk9E
3οΈβ£ Neural Networks: Zero to Hero
Now that youβve grasped the foundations of AI/ML, itβs time to dive deeper.
This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.
GitHub repo β https://lnkd.in/dXAQWucq
4οΈβ£ DL Paper Implementations
So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.
This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.
GitHub repo β https://lnkd.in/dTrtDrvs
5οΈβ£ Made With ML
Now itβs time to learn how to go from notebooks to production.
Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.
GitHub repo β https://lnkd.in/dYyjjBGb
6οΈβ£ Hands-on LLMs
- You've built neural nets.
- You've explored GPTs and LLMs.
Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.
GitHub repo β https://lnkd.in/dh2FwYFe
7οΈβ£ Advanced RAG Techniques
Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.
This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.
GitHub repo β https://lnkd.in/dBKxtX-D
8οΈβ£ AI Agents for Beginners by Microsoft
After diving into LLMs and mastering RAG, learn how to build AI agents.
This hands-on course covers building AI agents using frameworks like AutoGen.
GitHub repo β https://lnkd.in/dbFeuznE
9οΈβ£ Agents Towards Production
The above course will teach what AI agents are. Next, learn how to ship them.
This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.
GitHub repo β https://lnkd.in/dcwmamSb
π AI Engg. Hub
To truly master LLMs, RAG, and AI agents, you need projects.
This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.
GitHub repo β https://lnkd.in/geMYm3b6
(100% free step-by-step roadmap)
A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.
Includes quizzes, lessons, and hands-on projects, with some videos.
GitHub repo β https://lnkd.in/dCxStbYv
This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.
Beginner-friendly, project-based, and full of real-world apps.
GitHub repo β https://lnkd.in/dwS5Jk9E
Now that youβve grasped the foundations of AI/ML, itβs time to dive deeper.
This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.
GitHub repo β https://lnkd.in/dXAQWucq
So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.
This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.
GitHub repo β https://lnkd.in/dTrtDrvs
Now itβs time to learn how to go from notebooks to production.
Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.
GitHub repo β https://lnkd.in/dYyjjBGb
- You've built neural nets.
- You've explored GPTs and LLMs.
Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.
GitHub repo β https://lnkd.in/dh2FwYFe
Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.
This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.
GitHub repo β https://lnkd.in/dBKxtX-D
After diving into LLMs and mastering RAG, learn how to build AI agents.
This hands-on course covers building AI agents using frameworks like AutoGen.
GitHub repo β https://lnkd.in/dbFeuznE
The above course will teach what AI agents are. Next, learn how to ship them.
This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.
GitHub repo β https://lnkd.in/dcwmamSb
To truly master LLMs, RAG, and AI agents, you need projects.
This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.
GitHub repo β https://lnkd.in/geMYm3b6
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
βοΈ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBkπ± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Auto-Encoder & Backpropagation by hand βοΈ lecture video ~ πΊ https://byhand.ai/cv/10
It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.
= Chapters =
β’ Encoder & Decoder (00:00)
β’ Equation (10:09)
β’ 4-2-4 AutoEncoder (16:38)
β’ 6-4-2-4-6 AutoEncoder (18:39)
β’ L2 Loss (20:49)
β’ L2 Loss Gradient (27:31)
β’ Backpropagation (30:12)
β’ Implement Backpropagation (39:00)
β’ Gradient Descent (44:30)
β’ Summary (51:39)
βοΈ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.
= Chapters =
β’ Encoder & Decoder (00:00)
β’ Equation (10:09)
β’ 4-2-4 AutoEncoder (16:38)
β’ 6-4-2-4-6 AutoEncoder (18:39)
β’ L2 Loss (20:49)
β’ L2 Loss Gradient (27:31)
β’ Backpropagation (30:12)
β’ Implement Backpropagation (39:00)
β’ Gradient Descent (44:30)
β’ Summary (51:39)
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
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GPU by hand βοΈ I drew this to show how a GPU speeds up an array operation of 8 elements in parallel over 4 threads in 2 clock cycles. Read more π
CPU
β’ It has one core.
β’ Its global memory has 120 locations (0-119).
β’ To use the GPU, it needs to copy data from the global memory to the GPU.
β’ After GPU is done, it will copy the results back.
GPU
β’ It has four cores to run four threads (0-3).
β’ It has a register file of 28 locations (0-27)
β’ This register file has four banks (0-3).
β’ All threads share the same register file.
β’ But they must read/write using the four banks.
β’ Each bank allows 2 reads (Read 0, Read 1) and 1 write in a single clock cycle.
βοΈ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
CPU
β’ It has one core.
β’ Its global memory has 120 locations (0-119).
β’ To use the GPU, it needs to copy data from the global memory to the GPU.
β’ After GPU is done, it will copy the results back.
GPU
β’ It has four cores to run four threads (0-3).
β’ It has a register file of 28 locations (0-27)
β’ This register file has four banks (0-3).
β’ All threads share the same register file.
β’ But they must read/write using the four banks.
β’ Each bank allows 2 reads (Read 0, Read 1) and 1 write in a single clock cycle.
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
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