• What is the easiest way to create a public blog? https://mlfromscratch.com/probability-theory-bayes-theorem/ 4 comments 28/4/2020 webdev
• Introduction To Machine Learning Deployment Using Docker and Kubernetes https://mlfromscratch.com/deployment-introduction/ 21 comments 16/2/2020 datascience
• I scraped /r/DataScience and made a sample Machine Learning Project - for educational purposes! Here is all my takeaways and code, mostly written in Python with some SQL and Databases knowledge. I hope this helps someone learn what, how and why we do web scraping! https://mlfromscratch.com/web-scraping-machine-learning/ 10 comments 4/12/2019 datascience
• Explaining Feedforward, Backpropagation and Optimization: The Math Explained Clearly with Visualizations. I took the time to write this long article (>5k words), and I hope it helps someone understand neural networks better. https://mlfromscratch.com/neural-networks-explained/ 15 comments 30/11/2019 datascience
• TensorFlow 2.0 Tutorial in 10 Minutes - Operations (+ Linear Algebra) and CUSTOM training and testing classes. https://mlfromscratch.com/tensorflow-2/ 4 comments 6/11/2019 learnmachinelearning
• Explaning Activation Functions: Visualized and Math Explained Clearly. Code in Notebook along with Pros and Cons for GELU, SELU, ELU etc. I wrote this extensive article (>6k words) and I hope it helps you understand the activation functions better. https://mlfromscratch.com/activation-functions-explained/ 19 comments 3/9/2019 learnmachinelearning
• I explained Backpropagation and Optimization with Math and Visualizations in a very clear way. Now I'm looking for your suggestions, in which way I should take my Deep Learning content. https://mlfromscratch.com/neural-networks-explained/ 15 comments 19/8/2019 deeplearning
• I explained Backpropagation and Optimization with Math and Visualizations in a very clear way. Now I'm looking for your suggestions, in which way I should take my Deep Learning content. https://mlfromscratch.com/neural-networks-explained/ 18 comments 19/8/2019 datascience
• I explained Backpropagation and Optimization with Math and Visualizations in a very clear way. Now I'm looking for your suggestions, in which way I should take my Deep Learning content. https://mlfromscratch.com/neural-networks-explained/ 4 comments 19/8/2019 learnmachinelearning
• Explaining Feedforward, Backpropagation and Optimization: The Math Explained Clearly with Visualizations. I took the time to write this long article (>5k words), and I hope it helps someone understand neural networks better. https://mlfromscratch.com/neural-networks-explained/ 18 comments 5/8/2019 datascience
• Explaining Feedforward, Backpropagation and Optimization: The Math Explained Clearly with Visualizations. I took the time to write this long article (>5k words), and I hope it helps someone understand neural networks better. https://mlfromscratch.com/neural-networks-explained/ 13 comments 5/8/2019 learnmachinelearning