Aayush2009's Stars
rasbt/python-machine-learning-book-3rd-edition
The "Python Machine Learning (3rd edition)" book code repository
swaroopch/byte-of-python
Beginners book on Python - start here if you don't know programming
cosmicpython/book
A Book about Pythonic Application Architecture Patterns for Managing Complexity. Cosmos is the Opposite of Chaos you see. O'R. wouldn't actually let us call it "Cosmic Python" tho.
realpython/python-guide
Python best practices guidebook, written for humans.
Avik-Jain/100-Days-Of-ML-Code
100 Days of ML Coding
trekhleb/learn-python
📚 Playground and cheatsheet for learning Python. Collection of Python scripts that are split by topics and contain code examples with explanations.
jerry-git/learn-python3
Jupyter notebooks for teaching/learning Python 3
vinta/awesome-python
An opinionated list of awesome Python frameworks, libraries, software and resources.
rbhatia46/Data-Science-Interview-Resources
A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.
yash42828/Data-Science--All-Cheat-Sheet
ossu/data-science
📊 Path to a free self-taught education in Data Science!
practical-tutorials/project-based-learning
Curated list of project-based tutorials
rohitshewale302/Gold_Price_Forecasting
Predicted Gold Price for next 30 days, by using Machine Learning Algorithms - Arima, Holt-Winter, LSTM
AyeshaIrshad1337/Programming-With-AI
In this repository i will be sharing my python code from beginning to depth as their is no end xD
tensorflow/tensorflow
An Open Source Machine Learning Framework for Everyone
khangich/machine-learning-interview
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
ml-tooling/best-of-ml-python
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
patchy631/machine-learning
aymericdamien/TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
ujjwalkarn/Machine-Learning-Tutorials
machine learning and deep learning tutorials, articles and other resources
josephmisiti/awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
ZuzooVn/machine-learning-for-software-engineers
A complete daily plan for studying to become a machine learning engineer.
FavioVazquez/ds-cheatsheets
List of Data Science Cheatsheets to rule the world
kmario23/deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
donnemartin/data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
trekhleb/homemade-machine-learning
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
GokuMohandas/Made-With-ML
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Nyandwi/machine_learning_complete
A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
aishwaryanevrekar/aishwaryanevrekar
manyasrinivas2021/AI-BASED-FACIAL-EMOTION-DETECTION-USING-DEEP-LEARNING
“AI Based Facial Emotion Detection”, developed using many machine learning algorithms including convolution neural networks (CNN) for a facial expression recognition task. The goal is to classify each facial image into one of the seven facial emotion categories considered in this study.Trained CNN models with different depth using gray-scale images from the Kaggle website.CNN models are developed in Pytorch and exploited Graphics Processing Unit (GPU) computation in order to expedite the training process. In addition to the networks performing based on raw pixel data,Hybrid feature strategy is employed by which trained a novel CNN model with the combination of raw pixel data and Histogram of Oriented Gradients (HOG) features. To reduce the over fitting of the models,different techniques are utilized including dropout and batch normalization in addition to L2 regularization. Cross validation is applied to determine the optimal hyper-parameters and evaluated the performance of the developed models by looking at their training histories. Visualization of different layers of a network is presented to show what features of a face can be learned by CNN models. Based on the emotion the program recommends the music for the user to up flit the mood.