๐จ๐ปโ๐ป I've been collecting a variety of data science interview questions for different positions for a few weeks now.
Common Data Science and ML Questions (34 questions)
Regression (22 questions)
Classification (39 questions)
SVM algorithms, decision tree
Simple Bayes and statistical discussions and...
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#DataScience #InterviewPrep #MLInterviews #DataScientist #MachineLearning #TechCareers #DSInterviewQuestions #GitHubResources #CareerInDataScience #CodingInterview
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Anyone trying to deeply understand Large Language Models.
Checkout
by Tong Xiao & Jingbo Zhu. Itโs one of the clearest, most comprehensive resource.
โญ๏ธ Paper Link: arxiv.org/pdf/2501.09223
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Checkout
Foundations of Large Language Models
by Tong Xiao & Jingbo Zhu. Itโs one of the clearest, most comprehensive resource.
#LLMs #LargeLanguageModels #AIResearch #DeepLearning #MachineLearning #AIResources #NLP #AITheory #FoundationModels #AIUnderstanding
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โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Supervised Learning: Classification and Regression
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
#SupervisedLearning #MachineLearning #Classification #Regression #MLNotes #DataScience #AIResources #MLTheory #MLLectures #LearnML
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Self-attention in LLMs, clearly explained
#SelfAttention #LLMs #Transformers #NLP #DeepLearning #MachineLearning #AIExplained #AttentionMechanism #AIConcepts #AIEducation
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๐จ๐ปโ๐ป Real learning means implementing ideas and building prototypes. It's time to skip the repetitive training and get straight to real data science projects!
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#DataScience #PythonProjects #MachineLearning #DeepLearning #AIProjects #RealWorldData #OpenSource #DataAnalysis #ProjectBasedLearning #LearnByBuilding
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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๐ฌ๐ผ๐๐ฟ_๐๐ฎ๐๐ฎ_๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ_๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐_๐ฆ๐๐๐ฑ๐_๐ฃ๐น๐ฎ๐ป.pdf
7.7 MB
1. Master the fundamentals of Statistics
Understand probability, distributions, and hypothesis testing
Differentiate between descriptive vs inferential statistics
Learn various sampling techniques
2. Get hands-on with Python & SQL
Work with data structures, pandas, numpy, and matplotlib
Practice writing optimized SQL queries
Master joins, filters, groupings, and window functions
3. Build real-world projects
Construct end-to-end data pipelines
Develop predictive models with machine learning
Create business-focused dashboards
4. Practice case study interviews
Learn to break down ambiguous business problems
Ask clarifying questions to gather requirements
Think aloud and structure your answers logically
5. Mock interviews with feedback
Use platforms like Pramp or connect with peers
Record and review your answers for improvement
Gather feedback on your explanation and presence
6. Revise machine learning concepts
Understand supervised vs unsupervised learning
Grasp overfitting, underfitting, and bias-variance tradeoff
Know how to evaluate models (precision, recall, F1-score, AUC, etc.)
7. Brush up on system design (if applicable)
Learn how to design scalable data pipelines
Compare real-time vs batch processing
Familiarize with tools: Apache Spark, Kafka, Airflow
8. Strengthen storytelling with data
Apply the STAR method in behavioral questions
Simplify complex technical topics
Emphasize business impact and insight-driven decisions
9. Customize your resume and portfolio
Tailor your resume for each job role
Include links to projects or GitHub profiles
Match your skills to job descriptions
10. Stay consistent and track progress
Set clear weekly goals
Monitor covered topics and completed tasks
Reflect regularly and adapt your plan as needed
#DataScience #InterviewPrep #MLInterviews #DataEngineering #SQL #Python #Statistics #MachineLearning #DataStorytelling #SystemDesign #CareerGrowth #DataScienceRoadmap #PortfolioBuilding #MockInterviews #JobHuntingTips
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rnn.pdf
5.6 MB
๐ Understanding Recurrent Neural Networks (RNNs) Cheat Sheet!
Recurrent Neural Networks are a powerful type of neural network designed to handle sequential data. They are widely used in applications like natural language processing, speech recognition, and time-series prediction. Here's a quick cheat sheet to get you started:
๐ Key Concepts:
Sequential Data: RNNs are designed to process sequences of data, making them ideal for tasks where order matters.
Hidden State: Maintains information from previous inputs, enabling memory across time steps.
Backpropagation Through Time (BPTT): The method used to train RNNs by unrolling the network through time.
๐ง Common Variants:
Long Short-Term Memory (LSTM): Addresses vanishing gradient problems with gates to manage information flow.
Gated Recurrent Unit (GRU): Similar to LSTMs but with a simpler architecture.
๐ Applications:
Language Modeling: Predicting the next word in a sentence.
Sentiment Analysis: Understanding sentiments in text.
Time-Series Forecasting: Predicting future data points in a series.
๐ Resources:
Dive deeper with tutorials on platforms like Coursera, edX, or YouTube.
Explore open-source libraries like TensorFlow or PyTorch for implementation.
Let's harness the power of RNNs to innovate and solve complex problems!๐ก
Recurrent Neural Networks are a powerful type of neural network designed to handle sequential data. They are widely used in applications like natural language processing, speech recognition, and time-series prediction. Here's a quick cheat sheet to get you started:
๐ Key Concepts:
Sequential Data: RNNs are designed to process sequences of data, making them ideal for tasks where order matters.
Hidden State: Maintains information from previous inputs, enabling memory across time steps.
Backpropagation Through Time (BPTT): The method used to train RNNs by unrolling the network through time.
๐ง Common Variants:
Long Short-Term Memory (LSTM): Addresses vanishing gradient problems with gates to manage information flow.
Gated Recurrent Unit (GRU): Similar to LSTMs but with a simpler architecture.
๐ Applications:
Language Modeling: Predicting the next word in a sentence.
Sentiment Analysis: Understanding sentiments in text.
Time-Series Forecasting: Predicting future data points in a series.
๐ Resources:
Dive deeper with tutorials on platforms like Coursera, edX, or YouTube.
Explore open-source libraries like TensorFlow or PyTorch for implementation.
Let's harness the power of RNNs to innovate and solve complex problems!
#RNN #RecurrentNeuralNetworks #DeepLearning #NLP #LSTM #GRU #TimeSeriesForecasting #MachineLearning #NeuralNetworks #AIApplications #SequenceModeling #MLCheatSheet #PyTorch #TensorFlow #DataScience
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AI vs ML vs Deep Learning vs Generative AI
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#ArtificialIntelligence #MachineLearning #DeepLearning #GenerativeAI #AIVsML #AITechnology #LearnAI #AIExplained
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A curated collection of Kaggle notebooks showcasing how to build end-to-end AI applications using Hugging Face pretrained models, covering text, speech, image, and vision-language tasks โ full tutorials and code available on GitHub:
1๏ธโฃ Text-Based Applications
1.1. Building a Chatbot Using HuggingFace Open Source Models
https://lnkd.in/dku3bigK
1.2. Building a Text Translation System using Meta NLLB Open-Source Model
https://lnkd.in/dgdjaFds
2๏ธโฃ Speech-Based Applications
2.1. Zero-Shot Audio Classification Using HuggingFace CLAP Open-Source Model
https://lnkd.in/dbgQgDyn
2.2. Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio
https://lnkd.in/dcbp-8fN
2.3. Building Text-to-Speech Systems Using VITS & ArTST Models
https://lnkd.in/dwFcQ_X5
3๏ธโฃ Image-Based Applications
3.1. Step-by-Step Guide to Zero-Shot Image Classification using CLIP Model
https://lnkd.in/dnk6epGB
3.2. Building an Object Detection Assistant Application: A Step-by-Step Guide
https://lnkd.in/d573SvYV
3.3. Zero-Shot Image Segmentation using Segment Anything Model (SAM)
https://lnkd.in/dFavEdHS
3.4. Building Zero-Shot Depth Estimation Application Using DPT Model & Gradio
https://lnkd.in/d9jjJu_g
4๏ธโฃ Vision Language Applications
4.1. Building a Visual Question Answering System Using Hugging Face Open-Source Models
https://lnkd.in/dHNFaHFV
4.2. Building an Image Captioning System using Salesforce Blip Model
https://lnkd.in/dh36iDn9
4.3. Building an Image-to-Text Matching System Using Hugging Face Open-Source Models
https://lnkd.in/d7fsJEAF
โก๏ธ You can find the articles and the codes for each article in this GitHub repo:
https://lnkd.in/dG5jfBwE
1๏ธโฃ Text-Based Applications
1.1. Building a Chatbot Using HuggingFace Open Source Models
https://lnkd.in/dku3bigK
1.2. Building a Text Translation System using Meta NLLB Open-Source Model
https://lnkd.in/dgdjaFds
2๏ธโฃ Speech-Based Applications
2.1. Zero-Shot Audio Classification Using HuggingFace CLAP Open-Source Model
https://lnkd.in/dbgQgDyn
2.2. Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio
https://lnkd.in/dcbp-8fN
2.3. Building Text-to-Speech Systems Using VITS & ArTST Models
https://lnkd.in/dwFcQ_X5
3๏ธโฃ Image-Based Applications
3.1. Step-by-Step Guide to Zero-Shot Image Classification using CLIP Model
https://lnkd.in/dnk6epGB
3.2. Building an Object Detection Assistant Application: A Step-by-Step Guide
https://lnkd.in/d573SvYV
3.3. Zero-Shot Image Segmentation using Segment Anything Model (SAM)
https://lnkd.in/dFavEdHS
3.4. Building Zero-Shot Depth Estimation Application Using DPT Model & Gradio
https://lnkd.in/d9jjJu_g
4๏ธโฃ Vision Language Applications
4.1. Building a Visual Question Answering System Using Hugging Face Open-Source Models
https://lnkd.in/dHNFaHFV
4.2. Building an Image Captioning System using Salesforce Blip Model
https://lnkd.in/dh36iDn9
4.3. Building an Image-to-Text Matching System Using Hugging Face Open-Source Models
https://lnkd.in/d7fsJEAF
โก๏ธ You can find the articles and the codes for each article in this GitHub repo:
https://lnkd.in/dG5jfBwE
#HuggingFace #Kaggle #AIapplications #DeepLearning #MachineLearning #ComputerVision #NLP #SpeechRecognition #TextToSpeech #ImageProcessing #OpenSourceAI #ZeroShotLearning #Gradio
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โค13๐ฏ1
The 2025 MIT deep learning course is excellent, covering neural networks, CNNs, RNNs, and LLMs. You build three projects for hands-on experience as part of the course. It is entirely free. Highly recommended for beginners.
Enroll Free: https://introtodeeplearning.com/
Enroll Free: https://introtodeeplearning.com/
#DeepLearning #MITCourse #NeuralNetworks #CNN #RNN #LLMs #AIForBeginners #FreeCourse #MachineLearning #IntroToDeepLearning #AIProjects #LearnAI #AI2025
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LLM Interview Questions.pdf
71.2 KB
Top 50 LLM Interview Questions!
#LLM #AIInterviews #MachineLearning #DeepLearning #NLP #LLMInterviewPrep #ModelArchitectures #AITheory #TechInterviews #MLBasics #InterviewQuestions #LargeLanguageModels
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This book covers foundational topics within computer vision, with an image processing and machine learning perspective. We want to build the readerโs intuition and so we include many visualizations. The audience is undergraduate and graduate students who are entering the field, but we hope experienced practitioners will find the book valuable as well.
Our initial goal was to write a large book that provided a good coverage of the field. Unfortunately, the field of computer vision is just too large for that. So, we decided to write a small book instead, limiting each chapter to no more than five pages. Such a goal forced us to really focus on the important concepts necessary to understand each topic. Writing a short book was perfect because we did not have time to write a long book and you did not have time to read it. Unfortunately, we have failed at that goal, too.
Read it online: https://visionbook.mit.edu/
Our initial goal was to write a large book that provided a good coverage of the field. Unfortunately, the field of computer vision is just too large for that. So, we decided to write a small book instead, limiting each chapter to no more than five pages. Such a goal forced us to really focus on the important concepts necessary to understand each topic. Writing a short book was perfect because we did not have time to write a long book and you did not have time to read it. Unfortunately, we have failed at that goal, too.
Read it online: https://visionbook.mit.edu/
#ComputerVision #ImageProcessing #MachineLearning #CVBook #VisualLearning #AIResources #ComputerVisionBasics #MLForVision #AcademicResources #LearnComputerVision #AIIntuition #DeepLearning
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Over the last year, several articles have been written to help candidates prepare for data science technical interviews. These resources cover a wide range of topics including machine learning, SQL, programming, statistics, and probability.
1๏ธโฃ Machine Learning (ML) Interview
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37
ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0
Crack the ML Coding Q&A
https://shorturl.at/CDW08
Deep Learning Interview Q&A
https://shorturl.at/lHPZ6
Top LLMs Interview Q&A
https://shorturl.at/wGRSZ
Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi
Part 2
https://rb.gy/hqgkbg
Part 3
https://rb.gy/5z87be
2๏ธโฃ SQL Interview Preparation
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1
SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH
Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9
Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n
How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA
Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE
SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw
6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q
3๏ธโฃ Programming Questions
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq
Part 2
https://lnkd.in/gATY4rTT
Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5
Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A
Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN
Python Interview Q&A
https://lnkd.in/gcaXc_JE
5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd
4๏ธโฃ Statistics
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5
Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8
Introduction to A/B Testing
https://lnkd.in/g35Jihw6
Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q
5๏ธโฃ Probability
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk
Part 2
https://lnkd.in/gQhXnKwJ
Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp
Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj
๐ All links are available in the GitHub repository:
https://lnkd.in/djcgcKRT
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37
ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0
Crack the ML Coding Q&A
https://shorturl.at/CDW08
Deep Learning Interview Q&A
https://shorturl.at/lHPZ6
Top LLMs Interview Q&A
https://shorturl.at/wGRSZ
Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi
Part 2
https://rb.gy/hqgkbg
Part 3
https://rb.gy/5z87be
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1
SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH
Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9
Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n
How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA
Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE
SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw
6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq
Part 2
https://lnkd.in/gATY4rTT
Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5
Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A
Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN
Python Interview Q&A
https://lnkd.in/gcaXc_JE
5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5
Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8
Introduction to A/B Testing
https://lnkd.in/g35Jihw6
Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk
Part 2
https://lnkd.in/gQhXnKwJ
Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp
Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj
https://lnkd.in/djcgcKRT
#DataScience #InterviewPrep #MachineLearning #SQL #Python #Statistics #Probability #CodingInterview #AIBootcamp #DeepLearning #LLMs #ComputerVision #GitHubResources #CareerInDataScience
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ 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)
1๏ธโฃ 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 โ https://lnkd.in/dCxStbYv
2๏ธโฃ 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 โ https://lnkd.in/dwS5Jk9E
3๏ธโฃ Neural Networks: Zero to Hero
Now that youโve grasped the foundations of AI/ML, itโs time to dive deeper.
This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.
GitHub repo โ https://lnkd.in/dXAQWucq
4๏ธโฃ 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 โ https://lnkd.in/dTrtDrvs
5๏ธโฃ Made With ML
Now itโs 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 โ https://lnkd.in/dYyjjBGb
6๏ธโฃ 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 โ https://lnkd.in/dh2FwYFe
7๏ธโฃ 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 โ https://lnkd.in/dBKxtX-D
8๏ธโฃ 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 โ https://lnkd.in/dbFeuznE
9๏ธโฃ 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 โ https://lnkd.in/dcwmamSb
๐ 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 โ 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 โ 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 โ https://lnkd.in/dwS5Jk9E
Now that youโve grasped the foundations of AI/ML, itโs time to dive deeper.
This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.
GitHub repo โ 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 โ https://lnkd.in/dTrtDrvs
Now itโs 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 โ 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 โ 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 โ 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 โ 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 โ 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 โ https://lnkd.in/geMYm3b6
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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โค6
Auto-Encoder & Backpropagation by hand โ๏ธ lecture video ~ ๐บ 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 =
โข Encoder & Decoder (00:00)
โข Equation (10:09)
โข 4-2-4 AutoEncoder (16:38)
โข 6-4-2-4-6 AutoEncoder (18:39)
โข L2 Loss (20:49)
โข L2 Loss Gradient (27:31)
โข Backpropagation (30:12)
โข Implement Backpropagation (39:00)
โข Gradient Descent (44:30)
โข Summary (51:39)
โ๏ธ 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 =
โข Encoder & Decoder (00:00)
โข Equation (10:09)
โข 4-2-4 AutoEncoder (16:38)
โข 6-4-2-4-6 AutoEncoder (18:39)
โข L2 Loss (20:49)
โข L2 Loss Gradient (27:31)
โข Backpropagation (30:12)
โข Implement Backpropagation (39:00)
โข Gradient Descent (44:30)
โข Summary (51:39)
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
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โค3
Introduction to Deep Learning.pdf
10.5 MB
Introduction to Deep Learning
As we continue to push the boundaries of what's possible with artificial intelligence, I wanted to take a moment to share some insights on one of the most exciting fields in AI: Deep Learning.
Deep Learning is a subset of machine learning that uses neural networks to analyze and interpret data. These neural networks are designed to mimic the human brain, with layers of interconnected nodes (neurons) that process and transmit information.
What makes Deep Learning so powerful?
Ability to learn from large datasets: Deep Learning algorithms can learn from vast amounts of data, including images, speech, and text.
Improved accuracy: Deep Learning models can achieve state-of-the-art performance in tasks such as image recognition, natural language processing, and speech recognition.
Ability to generalize: Deep Learning models can generalize well to new, unseen data, making them highly effective in real-world applications.
Real-world applications of Deep Learning
Computer Vision: Self-driving cars, facial recognition, object detection
Natural Language Processing: Language translation, text summarization, sentiment analysis
Speech Recognition: Virtual assistants, voice-controlled devices.
#DeepLearning #AI #MachineLearning #NeuralNetworks #ArtificialIntelligence #DataScience #ComputerVision #NLP #SpeechRecognition #TechInnovation
As we continue to push the boundaries of what's possible with artificial intelligence, I wanted to take a moment to share some insights on one of the most exciting fields in AI: Deep Learning.
Deep Learning is a subset of machine learning that uses neural networks to analyze and interpret data. These neural networks are designed to mimic the human brain, with layers of interconnected nodes (neurons) that process and transmit information.
What makes Deep Learning so powerful?
Ability to learn from large datasets: Deep Learning algorithms can learn from vast amounts of data, including images, speech, and text.
Improved accuracy: Deep Learning models can achieve state-of-the-art performance in tasks such as image recognition, natural language processing, and speech recognition.
Ability to generalize: Deep Learning models can generalize well to new, unseen data, making them highly effective in real-world applications.
Real-world applications of Deep Learning
Computer Vision: Self-driving cars, facial recognition, object detection
Natural Language Processing: Language translation, text summarization, sentiment analysis
Speech Recognition: Virtual assistants, voice-controlled devices.
#DeepLearning #AI #MachineLearning #NeuralNetworks #ArtificialIntelligence #DataScience #ComputerVision #NLP #SpeechRecognition #TechInnovation
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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โค8
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GPU by hand โ๏ธ 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 ๐
CPU
โข It has one core.
โข Its global memory has 120 locations (0-119).
โข To use the GPU, it needs to copy data from the global memory to the GPU.
โข After GPU is done, it will copy the results back.
GPU
โข It has four cores to run four threads (0-3).
โข It has a register file of 28 locations (0-27)
โข This register file has four banks (0-3).
โข All threads share the same register file.
โข But they must read/write using the four banks.
โข Each bank allows 2 reads (Read 0, Read 1) and 1 write in a single clock cycle.
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
CPU
โข It has one core.
โข Its global memory has 120 locations (0-119).
โข To use the GPU, it needs to copy data from the global memory to the GPU.
โข After GPU is done, it will copy the results back.
GPU
โข It has four cores to run four threads (0-3).
โข It has a register file of 28 locations (0-27)
โข This register file has four banks (0-3).
โข All threads share the same register file.
โข But they must read/write using the four banks.
โข 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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๐4