Machine learning is complex. For newbies, starting to learn machine learning can be painful if they don’t have right resources to learn from. Most of the machine learning libraries are difficult to understand and learning curve can be a bit frustrating. I am creating a repository on Github(cheatsheets-ai) containing cheatsheets for different machine learning frameworks, gathered from different sources. Do visit the Github repository, also, contribute cheat sheets if you have any. Thanks.
List of Cheatsheets: 1. Keras 2. Numpy 3. Pandas 4. Scipy 5. Matplotlib 6. Scikit-learn 7. Neural Networks Zoo 8. ggplot2 9. PySpark 10. R Studio 11. Jupyter Notebook 12. Dask
7. Neural Networks Zoo
10. R Studio (dplyr and tidyr)
11. Jupyter Notebook
Thank you for reading.
If you want to get into contact, you can reach out to me at email@example.com
I am a Co-Founder of MateLabs, where we have built Mateverse, an ML Platform which enables everyone to easily build and train Machine Learning Models, without writing a single line of code.
Note: Recently, I published a book on GANs titled “Generative Adversarial Networks Projects”, in which I covered most of the widely popular GAN architectures and their implementations. DCGAN, StackGAN, CycleGAN, Pix2pix, Age-cGAN, and 3D-GAN have been covered in details at the implementation level. Each architecture has a chapter dedicated to it. I have explained these networks in a very simple and descriptive language using Keras framework with Tensorflow backend. If you are working on GANs or planning to use GANs, give it a read and share your valuable feedback with me at firstname.lastname@example.org
Gradient descent (GD) is an optimization algorithm that was derived by solving the quadratic programming (QP). A set of input values, an initial value for the output value, and its cost are required to implement GD, which is specified