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Places to consume your daily AI

2024.04.06 [Updated: 2024.04.06] :: #ai #papers

Find your Research Papers§

  1. Arxiv ai
  2. Papers with code
  3. Hugging Face Paper Of the day
  4. Deep Learning Monitor
  5. Top Ml papers of the week
  6. Papers from google
  7. Deep mind papers
  8. Meta papers
  9. OpenAI papers
  10. Stability ai research

Conferences§

  1. List for 2024

Easy Dose Of AI§

  1. The Neuron
  2. The Gradiant
  3. Alpha singnal
  4. The Batch

Blogs§

  1. BAIR
  2. Stanford AI
  3. Deepmind
  4. Deep Learning Focus
  5. Import Ai
  6. Ahead Of Ai - Papers of the month
  7. Exploring language Models

Reddit§

  1. r/MachineLearning
  2. r/languagemodels
  3. r/LocalLLaMA
  4. r/learnmachinelearning

Free Courses§

  1. UT-CS388
  2. CMU-601sp15
  3. Tübingen Machine Learning
  4. Andrej Karpathy
  5. 3Blue1Brown
  6. Samuel Albanie
  7. Pascal Poupart

The following links are taken from here

Video Lectures for Machine Learning(Theory):§

Machine Learning:§

  1. Cornell CS4780
  2. Stanford CS 229
  3. IIT Madras
  4. IISc Bangalore(Rigorous Math)
  5. Applied Machine Learning Cornell CS5787:
  6. Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa:
  7. StatQuest(Best resource for revision and visualization)

Deep Learning:§

  1. IIT Madras(No prerequisites and great prof)
    1. Part 1
    2. Part 2
  2. Course link for slides and references
  3. Neural Networks by Hinton
  4. NYU DL (Taught by Prof Alfredo Canziani and Prof Yann Lecun)

Computer Vision(Deep Learning):§

  1. Michigan University (This Michigan university course is the updated version of Stanford’s CS231n CV course and includes all the content covered by that as well)
  2. Advanced Deep Learning for Computer Vision by TU Munich

Natural Language Processing(Deep Learning):§

  1. Stanford CS 224n
  2. Natural Language Understanding Stanford CS 224u
  3. Deep Learning for NLP at Oxford with Deep Mind 2017
  4. NLP CMU 11-411/11-611
  5. CMU CS11-737 Multilingual Natural Language Processing

Reinforcement Learning:§

  1. IIT Madras
  2. Stanford CS234

Deep Reinforcement Learning:§

  1. UC Berkeley CS 285

Other:§

  1. CS224W: Machine Learning with Graphs
  2. Stanford CS330: Multi-Task and Meta-Learning
  3. Explainable AI
  4. Explainable AI in Industry

Some Math lectures(refresher):§

  1. Linear algebra(MIT)
  2. Optimization(IIT Kanpur)
  3. Multivariable Calculus(MIT)
  4. Probability and Statistics(Harvard)

Industry courses§

credits

  1. Courses from Google
  2. Microsoft Ai Course
  3. Intro to Ai with Python
  4. Prompt engineering with GPT
  5. GPT for devs

Apps§

  1. [ArXivly on Appstore]
  2. [Arx on Appstore]
  3. https://arxiv.aidev.run
  4. Connect your paper to connected papers to find more.

Projects§

  1. https://cloud.epsilla.com/enterprise-search/df6624c1-1c2a-4263-8c10-14f495fc3e7f-340417431/73ee9739-e674-44cd-b007-3c46b5811c2b?src=reddit
  2. https://elicit.com/?redirected=true