Probability, Statistics, Linear Algebra and Calculus. Checkout the Github Repo → http://
github.com/afshinea/stanf
ord-cs-229-machine-learning
…
@sumanth_077
-
Essential Mathematics for Machine Learning: GitHub Repository Guide
By
–
-
Deep Learning and Machine Learning Tips and Tricks
By
–
Deep Learning, Tips and Tricks in Machine Learning
-
Stanford releases cheatsheets for ML, Deep Learning, Statistics, Linear Algebra
By
–
Stanford University has released these cheatsheets on Machine Learning, Deep Learning, Statistics, and Linear Algebra. Use this as your reference next time working with them:
-
ReLU Activation Function Explained: Solving Vanishing Gradient Problem
By
–
Rectified Linear Unit (ReLU) function clearly explained! In Neural Network one of the biggest challenge using Sigmoid Activation Function is Vanishing Gradient Descent. But ReLU solves this. Let me explain you how? While Back Propogation, with sigmoid activation function the
-
MLX packages for neural networks and optimization are convenient
By
–
@tinztwins it's good. Especially the packages like mlx.nn and mlx.optimizers are handy!
-
Apple Releases MLX Deep Learning Framework for macOS Machines
By
–
Apple released MLX: A deeplearning framework for machine learning on macOS! You can efficiently train and deploy ML models. The repo also includes examples to run open-source models using MLX, like: – LLaMA 2
– Mixtral 8x7B
– LoRA
– CLIP
– Whisper
– Stable Diffusion -
Wrapping up daily content sharing on Python and Data Science
By
–
That's a wrap! Every day, I share and simply content around Python, Data Science, Machine Learning & Large Language Models. Find me → @Sumanth_077 Like/RT the first tweet to help this reach more people.
-
Advanced Data Cleaning Techniques for Missing Values and Duplicates
By
–
Data Cleaning: This also helps you to use the advanced data cleaning techniques such as locating missing values, standardizing formats, removing duplicates.
-
Machine Learning and Predictive Analytics with Python Libraries
By
–
Machine Learning, Predictive Analytics, and Forecasting: You can leverage Python libraries like scikit-learn & statsmodels to apply machine learning, predictive analytics, and forecasting techniques such as regression analysis, time series modeling, and more.