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deeplearning-notes
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www.mlcompendium.com
- The traveling salesman problem: low-dimensionality implies a polynomial.. (2012)
- Machine & Deep Learning Compendium
- The Machine and Deep Learning Compendium
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Last week fluff-free AI, ML, and data-related original articles summary
- The (Interactive) Machine & Deep Learning Compendium link
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[P] The ML & DL Compendium: Revamped!
Or on GitHub if you want to contribute (or star it): https://github.com/orico/www.mlcompendium.com/
deeplearning-notes
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Intuition for LSTM cell structure
If you want in-depth understanding then I would recommend you to look for Deep Learning Specialization by Andrew Ng. (Course 4). He explained the LSTM and GRU cells in detail (mathematically). You can also find it on YouTube I guess. Hope it helps.
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[D] Best deep learning course?
Best place to get started https://www.coursera.org/specializations/deep-learning
- Which course from deeplearning.ai should I take first? There are so many now
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Where to go from here
I want to expand on what I learnt theory and practice to be able to complete a project, where I can download a video and run it through my model and it will be able highlight specified items, e.g people trees, cars. Will this course help me get there https://www.coursera.org/specializations/deep-learning
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This is my self-learning curriculum for ML. Hope it helps and open to feedback!
Another one from DeepLearning.ai and this is also the most popular course for Deep Learning and Neural Networks - https://www.coursera.org/specializations/deep-learning
- AI roadmap
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Assignments to practice for course "neural-networks-deep-learning"
This course is a part of one of the 5 courses in DL specialization: https://www.coursera.org/specializations/deep-learning. I am taking this course on Coursera where I have finished up to week 2. Now I need to practice for it, but I think I can't access assignments as its locked for paid viewers. Can someone share me the resources for practice or any alternatives you found useful?
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Coursera or Udacity for TF developer certificate
There is another [course] (https://www.coursera.org/specializations/deep-learning) by deeplearning ai that catched my eye and in review they say its more in detail than tf in practice course.
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Career in Computer Vision - Best way to spool up through OMSCS
Deep learning like others said, but I've seen some posts recommending taking an external class, like Andrew Ng's Coursera class https://www.coursera.org/specializations/deep-learning over the GT one. I haven't taken or plan on taking the GT one but some people found it lacking
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How relevant is “A super harsh guide to machine learning” for someone who is just tinkering with machine learning?
My recommendations are worth little, I'm just starting through all this stuff myself. I'm currently taking the Deep Learning specialization on Coursera and trying to map out what else I should be doing.
What are some alternatives?
Data-Science-Roadmap - Data Science Roadmap from A to Z
coursera-deep-learning-specialization - Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
php-nwsl - Create responsive HTML emails (Newsletters) with a few lines of PHP code.
Credit_Card_Data_Clustering - Using Gaussian Clustering and PCA Techniques to make clusters of the Credit Car data
Breast_Cancer_DecisionTree_Classifier
ml-coursera-python-assignments - Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions.
micrograd - A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
NNfSiX - Neural Networks from Scratch in various programming languages
course-nlp - A Code-First Introduction to NLP course
qubes-thinkpad-x1-extreme-gen3 - Files and notes to install/run Qubes 4.1 on a ThinkPad X1 Extreme Gen3
ML-From-Scratch - Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Healthcare_dataset_pandas_preprocessing