Leetcode for machine learning #ai #machinelearning #coding

Elvis Saravia · Beginner ·📐 ML Fundamentals ·1y ago

Key Takeaways

The video discusses a website that offers machine learning coding challenges, similar to LeetCode, to help machine learning engineers practice and prepare for interviews or exams, utilizing concepts from Deep-ML and other resources.

Full Transcript

if you ever had to practice for a software engineering interview you typically start off with completing coding challenges on Le code or what would be the equivalent of these coding challenges for a machine learning engineer I haven't seen too many solutions on this until now this new website includes a set of machine learning coding challenges to assess your understanding of important Concepts in ml you can see the challenges are tagged by category and difficulty it also tracks how many problems you have solved very neat I think this is really use useful and can be a good starting point to prepare for an ml exam or interview do check it out Link in the description if you enjoy learning about interesting AI tools papers educational content follow for more

Original Description

Machine learning coding challenges. https://www.deep-ml.com/ #ai #machinelearning #coding
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Playlist

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101 ways to solve search (by Pratik Bhavsar)
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 9 Dive into Deep Learning (Study Group): Introduction to Deep Learning | Session 1
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 10 Dive into Deep Learning (Study Group): Multilayer Perceptrons | Session 4
Dive into Deep Learning (Study Group): Multilayer Perceptrons | Session 4
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 12 Keep Learning ML (Session 1) | DSV, CompLex, Modern tools for emotions
Keep Learning ML (Session 1) | DSV, CompLex, Modern tools for emotions
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 13 Dive into Deep Learning (Study Group): Preliminaries | Session 2
Dive into Deep Learning (Study Group): Preliminaries | Session 2
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 14 Keep Learning ML #2 | Language-conditioned policy learning, Effective ML Testing, EagerPy
Keep Learning ML #2 | Language-conditioned policy learning, Effective ML Testing, EagerPy
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 15 Dive into Deep Learning (Study Group): Linear Neural Networks | Session 3
Dive into Deep Learning (Study Group): Linear Neural Networks | Session 3
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 16 Dive into Deep Learning (Study Group): Multilayer Perceptrons | Session 4
Dive into Deep Learning (Study Group): Multilayer Perceptrons | Session 4
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 17 Keep Learning ML #3 | Contrastively Trained Structured World Models
Keep Learning ML #3 | Contrastively Trained Structured World Models
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 18 Dive into Deep Learning (Study Group): Deep Learning Computation with PyTorch | Session 5
Dive into Deep Learning (Study Group): Deep Learning Computation with PyTorch | Session 5
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 19 Dive into Deep Learning (Study Group): Convolutional Neural Networks | Session 6
Dive into Deep Learning (Study Group): Convolutional Neural Networks | Session 6
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 20 Dive into Deep Learning (Study Group): Modern CNNs | Session 7
Dive into Deep Learning (Study Group): Modern CNNs | Session 7
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 21 101 ways to solve neural search with Jina
101 ways to solve neural search with Jina
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 22 (Hopefully-Reusable) Life Lessons for PhD Students in NLP
(Hopefully-Reusable) Life Lessons for PhD Students in NLP
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 23 How to save the world and forward your career in 5 easy steps | Women in NLP Talks
How to save the world and forward your career in 5 easy steps | Women in NLP Talks
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 24 Prompt Engineering Overview
Prompt Engineering Overview
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 25 Getting Started with the OpenAI Playground
Getting Started with the OpenAI Playground
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 26 LM-Guided Chain of Thought
LM-Guided Chain of Thought
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 27 Elements of a Prompt
Elements of a Prompt
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 28 Reasoning with Intermediate Revision and Search with LLMs #chatgpt #ai #llms #science #programming
Reasoning with Intermediate Revision and Search with LLMs #chatgpt #ai #llms #science #programming
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 29 General Tips for Designing Prompts
General Tips for Designing Prompts
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 30 Efficient Infinite Context Transformers #ai #machinelearning #research #llms #science
Efficient Infinite Context Transformers #ai #machinelearning #research #llms #science
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 31 Best Practices and Lessons Learned on Synthetic Data for Language Models #ai #machinelearning #genai
Best Practices and Lessons Learned on Synthetic Data for Language Models #ai #machinelearning #genai
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 32 Reducing Hallucinations in Structured Outputs via RAG #chatgpt #ai #llms #programming
Reducing Hallucinations in Structured Outputs via RAG #chatgpt #ai #llms #programming
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 33 Basic Prompt Examples for LLMs
Basic Prompt Examples for LLMs
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 34 LLM In Context Recall is Prompt Dependent #llms #ai #chatgpt #machinelearning
LLM In Context Recall is Prompt Dependent #llms #ai #chatgpt #machinelearning
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 35 Zero-shot Prompting Explained
Zero-shot Prompting Explained
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 36 RAG Faithfulness #llms #ai #gpt4
RAG Faithfulness #llms #ai #gpt4
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 37 Understanding LLM Settings
Understanding LLM Settings
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 38 Llama 3 is here! | First impressions and thoughts
Llama 3 is here! | First impressions and thoughts
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 39 Llama 3 is Here! #ai #llms #llama3
Llama 3 is Here! #ai #llms #llama3
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 40 Microsoft introduces Phi-3 | The most capable small language model?
Microsoft introduces Phi-3 | The most capable small language model?
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 41 Microsoft introduces Phi-3! #ai #llms #microsoft
Microsoft introduces Phi-3! #ai #llms #microsoft
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 42 Make Your LLM Fully Utilize the Context #ai #llms #machinelearning
Make Your LLM Fully Utilize the Context #ai #llms #machinelearning
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 43 When to Retrieve? #ai #llms #machinelearning
When to Retrieve? #ai #llms #machinelearning
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 44 Training an LLM to effectively use information retrieval
Training an LLM to effectively use information retrieval
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 45 State-of-the-art open-source LLM judges #ai #machinelearning #gpt4
State-of-the-art open-source LLM judges #ai #machinelearning #gpt4
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 46 Better and Faster LLMs via Multi-token Prediction
Better and Faster LLMs via Multi-token Prediction
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 47 AlphaMath Almost Zero #ai #science #machinelearning
AlphaMath Almost Zero #ai #science #machinelearning
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 48 SWE-Agent | An LLM-based Software Engineering Agent
SWE-Agent | An LLM-based Software Engineering Agent
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 49 [LLM NEWS] AlphaFold 3, xLSTM, OpenAI's Model Spec, DeepSeek-V2, OpenDevin CodeAct 1.0
[LLM NEWS] AlphaFold 3, xLSTM, OpenAI's Model Spec, DeepSeek-V2, OpenDevin CodeAct 1.0
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 50 LLM-powered tool for web scraping #ai #chatgpt #engineering
LLM-powered tool for web scraping #ai #chatgpt #engineering
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 51 Learn about LLMs in this NEW course #ai #chatgpt #engineering
Learn about LLMs in this NEW course #ai #chatgpt #engineering
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 52 [LLM NEWS] KANs, Gemma 10M Context, OpenAI Updates?, Automatic Prompt Engineering, Tokenizer Arena
[LLM NEWS] KANs, Gemma 10M Context, OpenAI Updates?, Automatic Prompt Engineering, Tokenizer Arena
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 53 [LLM News] GPT4-o, Project Astra, Veo, Copilot+ PCs, Gemini 1.5 Flash, Chameleon
[LLM News] GPT4-o, Project Astra, Veo, Copilot+ PCs, Gemini 1.5 Flash, Chameleon
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 54 Enhancing Answer Selection in LLMs #ai #machinelearning #engineering
Enhancing Answer Selection in LLMs #ai #machinelearning #engineering
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 55 On exploring LLMs #ai #promptengineering #chatgpt
On exploring LLMs #ai #promptengineering #chatgpt
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 56 Transformers Can Do Arithmetic with the Right Embeddings #ai #machinelearning #engineering
Transformers Can Do Arithmetic with the Right Embeddings #ai #machinelearning #engineering
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 57 [LLM News] xAI Series B, Codestral, LLM Guide, AutoGen Course, Symbolic Chain-of-Thought
[LLM News] xAI Series B, Codestral, LLM Guide, AutoGen Course, Symbolic Chain-of-Thought
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 58 PR-Agent #ai #gpt4 #software
PR-Agent #ai #gpt4 #software
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 59 Extracting features from Claude 3 Sonnet
Extracting features from Claude 3 Sonnet
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 60 Has prompt engineering been solved?
Has prompt engineering been solved?
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The video introduces a website that provides machine learning coding challenges to help engineers practice and prepare for interviews or exams. The challenges are categorized by difficulty and track progress, making it a useful resource for ML engineers. By utilizing this resource, engineers can improve their coding skills and prepare for ML engineering roles.

Key Takeaways
  1. Visit the Deep-ML website
  2. Explore the machine learning coding challenges
  3. Start solving challenges by category and difficulty
  4. Track progress and identify areas for improvement
  5. Use the challenges to prepare for ML interviews or exams
💡 Practicing machine learning coding challenges can help engineers improve their skills and prepare for interviews or exams, and utilizing resources like Deep-ML can provide a structured approach to learning and improvement.

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