machine.academy
deeplearning-notes
| machine.academy | deeplearning-notes | |
|---|---|---|
| 1 | 71 | |
| 11 | 353 | |
| - | - | |
| 7.6 | 0.0 | |
| 5 months ago | over 1 year ago | |
| C# | ||
| GNU General Public License v3.0 only | MIT License |
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machine.academy
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Neural Network from Scratch
Nice! I made a gpu accelerated backpropagation lib a while ago to learn about NNs, if you are interested check it out here: https://github.com/zbendefy/machine.academy
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?
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.
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
micrograd - A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
Credit_Card_Data_Clustering - Using Gaussian Clustering and PCA Techniques to make clusters of the Credit Car data
GazeTracker - Artificial intelligence tracker for OpenTrack
Breast_Cancer_DecisionTree_Classifier
Numerical-Realization - An English-based natural language generator for numerical inputs.
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.
deepnet - Educational deep learning library in plain Numpy.
SoftGround - High performance runtime collider deformation in Unity
NNfSiX - Neural Networks from Scratch in various programming languages