316.3K
COMachine Learning, explained by cats. #cat #code #ai #machinelearning #algorithm
@coding.kitty
#Machine Learning Algorithm
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CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
54.9K
AILogistic regression is an algorithm and statistical method used in machine learning for binary classification tasks, where the goal is to predict one of two possible outcomes (for example, yes or no).
Logistic regression outputs probabilities that a certain event will occur, using the logistic function to transform the linear output of a model into a probability value from 0 to 1. It’s used mainly for cases where the variable is categorical.
The model learns from the data by finding the best set of weights (also the coefficients of the curve) that minimize the error between the model’s predicted and actual values.
Logistic regression is useful since it’s simple, easy to interpret, and effective for simple classification problems and serves as a good introduction to machine learning.
C: 3 minute data science
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#datascientist #computerengineering #deeplearning #computerscience #math #mathematics #ml #logisticregression #machinelearning #datascience #education #coding #programming #learning #courses #bootcamp #course
@aibutsimple
570.6K
THNeural networks and machine learning.
@theartificialintelligence
1.3M
SAI’ve been asked many times where to start learning ML, so after talking to so many experts in this field, this is a good place to start.
Comment down below “TRAIN” and I’ll send you a more in-depth checklist along with the best GitHub links to help you start learning each concept.
If you don’t receive the link you either need to follow first then comment, or your instagram is outdated. Either way, no worries. send me a dm and I’ll get it to you ASAP.
#cs #ai #dev #university #softwareengineer #viral #advice #machinelearning
@sambhav_athreya
3.0M
ITcomment ‘AI’ and I’ll send you the link in your DMs
this is such a great resource to guide you on your AI/ML journey!
#techcareer #ai #machinelearning #careergrowthtips #datascience #coding
@itsallykrinsky
132.4K
KRVisualizing the architecture of intelligence. 🕸️✨
Every neural network is built on the same fundamental concept: Layers.
🟡 Input Layer: Receives the raw data (pixels, text, numbers).
🟢 Hidden Layers: Where the magic happens—processing features and finding patterns.
🟠 Output Layer: Delivers the final prediction or decision.
From the simple Perceptron to the complex loops of an RNN, these structures are the blueprints for how machines learn. 📐
#NeuralNetworks #MachineLearning #DeepLearning #DataScience #AI #Education #Visualized
@kreggscode
158.2K
EQThe Secret Behind Machine Learning Predictions! Ever wondered how machines make binary decisions? This video breaks down Logistic Regression using the Sigmoid Function. We visualize how the weight (w) controls the steepness of the curve and how the bias (b) shifts it along the x-axis. See how Cross-Entropy (CE) Loss is minimized to find the optimal fit for your data points. Finally, we explore the decision boundary at P=0.5, which separates predictions into Class 0 and Class 1. Perfect for data science students and machine learning enthusiasts looking for a quick, intuitive visualization of classification algorithms and mathematical optimization. #LogisticRegression #MachineLearning #SigmoidFunction #Math #Manim
@equationsinmotion
58.6K
EMThese are some of the best beginner-friendly resources I’ve found to actually understand machine learning.
Nothing overly complicated, just what you need to get the concepts and start building.
Comment ML and I’ll send you all the resources.
@emrcodes
12.0K
KAWhat is xgboost in machine learning and why it beats random forest #ml #datascientist
@karinadatascientist
1.4M
INGraham scan algorithm animated!
Full video in the YouTube channel
#algorithms #computerscience #programming
@inside.code
706.2K
AILarge Language Models (LLMs) such as ChatGPT are based on neural networks called transformers, an architecture built using multiple attention mechanisms and multilayer perceptrons (MLPs).
These models process input text by learning context through self-attention mechanisms, which weighs the importance of each pair of words. This way, long sequences are no longer an issue.
This contextual understanding is passed through MLPs, which learn the representations and patterns of the sequence.
To generate text, the model generates a probability distribution of the next word; we choose the highest-probability word and keep predicting the next word, iterating to create a sentence or paragraph.
C: 3blue1brown
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@aibutsimple
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