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5 years ago Regression in Machine Learning https://medium.com/datadriveninvestor/regression-in-machine-learning-296caae933ec
Multi-Class classification with Sci-kit learn & XGBoost: A case study using Brainwave data https://medium.freecodecamp.org/multi-class-classification-with-sci-kit-learn-xgboost-a-case-study-using-brainwave-data-363d7fca5f69
Seq2Seq in Keras for Petrol Price Prediction using Italian Open Data https://medium.com/isiway-tech/seq2seq-in-keras-for-petrol-price-prediction-using-italian-open-data-767cb1016af3
Monte Carlo Method and Importance Sampling in Python https://medium.com/future-vision/monte-carlo-method-and-importance-sampling-in-python-ed3425248867
The Perceptron — A Building Block of Neural Networks https://blog.usejournal.com/the-perceptron-the-building-block-of-neural-networks-5a428d3f451d
Transfer Learning for Image Classification using Keras https://towardsdatascience.com/transfer-learning-for-image-classification-using-keras-c47ccf09c8c8
Predicting Irish electricity consumption with neural networks https://towardsdatascience.com/predicting-irish-electricity-consumption-with-neural-networks-7d179efea872
Essentials of Deep Learning : Introduction to Long Short Term Memory https://www.analyticsvidhya.com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/
How to Develop LSTM Models for Time Series Forecasting https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/
Image Completion with Deep Learning in TensorFlow http://bamos.github.io/2016/08/09/deep-completion/
Plotting Data With Seaborn and Pandas https://hackingandslacking.com/plotting-data-with-seaborn-and-pandas-d2499fdf6f01
Math & Neural Network from scratch in Python https://medium.com/datadriveninvestor/math-neural-network-from-scratch-in-python-d6da9f29ce65
Separating mixed signals with Independent Component Analysis https://towardsdatascience.com/separating-mixed-signals-with-independent-component-analysis-38205188f2f4
Principal Component Analysis — Math and Intuition https://towardsdatascience.com/principal-component-analysis-math-and-intuition-post-2-1849090e6b7a
Build Your Own Convolution Neural Network in 5 mins https://towardsdatascience.com/build-your-own-convolution-neural-network-in-5-mins-4217c2cf964f
The mathematics and Intuitions of Principal Component Analysis (PCA) Using Truncated Singular https://medium.com/the-data-league/the-mathematics-and-intuitions-of-principal-component-analysis-pca-using-truncated-singular-b4944e5e95e6
Unsupervised deep learning for data interpolation https://medium.com/@aliaksei.mikhailiuk/unsupervised-learning-for-data-interpolation-e259cf5dc957
Understanding Generative Adversarial Networks (GANs) https://towardsdatascience.com/understanding-generative-adversarial-networks-gans-cd6e4651a29
CNNs, Part 1: An Introduction to Convolutional Neural Networks https://victorzhou.com/blog/intro-to-cnns-part-1/
Extreme Event Forecasting with LSTM Autoencoders https://towardsdatascience.com/extreme-event-forecasting-with-lstm-autoencoders-297492485037
The Central Limit Theorem and its Implications https://towardsdatascience.com/the-central-limit-theorem-and-its-implications-4a7adac9d6de
Deep learning explained | InfoWorld https://www.infoworld.com/article/3397142/deep-learning-explained.amp.html
A step by step explanation of Principal Component Analysis https://towardsdatascience.com/a-step-by-step-explanation-of-principal-component-analysis-b836fb9c97e2
Handling imbalanced datasets in machine learning https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28
Expose vs publish: Docker port commands explained simply https://medium.com/the-code-review/expose-vs-publish-docker-port-commands-explained-simply-434593dbc9a3
Understanding Neural Networks: What, How and Why? https://towardsdatascience.com/understanding-neural-networks-what-how-and-why-18ec703ebd31
Where did the least-square come from? https://towardsdatascience.com/where-did-the-least-square-come-from-3f1abc7f7caf
An A-Z of useful Python tricks https://medium.com/free-code-camp/an-a-z-of-useful-python-tricks-b467524ee747
[GOOD] Artificial Neural Network and it’s contribution to Machine Learning — A beginner’s hand-book https://blog.goodaudience.com/artificial-neural-networks-and-its-contribution-to-machine-learning-a-beginner-s-hand-book-ab7f4e7b230e
MCMC Intuition for Everyone – Towards Data Science https://towardsdatascience.com/mcmc-intuition-for-everyone-5ae79fff22b1
Dress Segmentation with Autoencoder in Keras https://towardsdatascience.com/dress-segmentation-with-autoencoder-in-keras-497cf1fd169a
Speech Emotion Recognition with Convolution Neural Network https://towardsdatascience.com/speech-emotion-recognition-with-convolution-neural-network-1e6bb7130ce3
To understand LSTM architecture, code a forward pass with just NumPy
Support Vector Machine https://mail.google.com/mail/u/0/#inbox/FMfcgxwCgpfbxRzKpcScTSCfBZLgTDNt
The Data Science Interview Study Guide https://medium.com/better-programming/the-data-science-interview-study-guide-c3824cb76c2e https://towardsdatascience.com/the-lstm-reference-card-6163ca98ae87
Image Classification using Convolutional Neural Networks in Keras https://www.learnopencv.com/image-classification-using-convolutional-neural-networks-in-keras/ https://becominghuman.ai/visualizing-representations-bd9b62447e38
Regression: predict fuel efficiency https://www.tensorflow.org/tutorials/keras/basic_regression
Creating Diagnostic Plots in Python https://robert-alvarez.github.io/2018-06-04-diagnostic_plots/
Keras: Regression-based neural networks https://datascienceplus.com/keras-regression-based-neural-networks/ https://hackernoon.com/build-your-first-neural-network-to-predict-house-prices-with-keras-3fb0839680f4 https://machinelearningmastery.com/regression-tutorial-keras-deep-learning-library-python/ https://machinelearningmastery.com/how-to-make-classification-and-regression-predictions-for-deep-learning-models-in-keras/ https://towardsdatascience.com/building-a-deep-learning-model-using-keras-1548ca149d37 https://hackernoon.com/everything-you-need-to-know-about-neural-networks-8988c3ee4491
Usage of optimizers https://keras.io/optimizers/ https://machinelearningmastery.com/weighted-average-ensemble-for-deep-learning-neural-networks/ https://towardsdatascience.com/activation-functions-neural-networks-1cbd9f8d91d6
Generative Adversarial Networks using Tensorflow https://machinelearningmastery.com/regression-tutorial-keras-deep-learning-library-python/
Neural ODEs: breakdown of another deep learning breakthrough https://towardsdatascience.com/neural-odes-breakdown-of-another-deep-learning-breakthrough-3e78c7213795
An Intuitive Explanation to AutoEncoders https://towardsdatascience.com/autoencoders-in-keras-273389677c20
CatBoost vs. Light GBM vs. XGBoost https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db
Building a k-Nearest-Neighbors (k-NN) Model with Scikit-learn https://towardsdatascience.com/building-a-k-nearest-neighbors-k-nn-model-with-scikit-learn-51209555453a
Complete Guide of Activation Functions https://towardsdatascience.com/complete-guide-of-activation-functions-34076e95d044
Analyzing Text Classification Techniques on Youtube Data https://towardsdatascience.com/analyzing-text-classification-techniques-on-youtube-data-7af578449f58
Step-by-step understanding LSTM Autoencoder layers https://towardsdatascience.com/step-by-step-understanding-lstm-autoencoder-layers-ffab055b6352
Advanced Topics in Deep Convolutional Neural Networks https://towardsdatascience.com/advanced-topics-in-deep-convolutional-neural-networks-71ef1190522d
RNN Simplified- A beginner’s guide https://towardsdatascience.com/rnn-simplified-a-beginners-guide-cf3ae1a8895b
Installation https://medium.com/@vivek.yadav/deep-learning-setup-for-ubuntu-16-04-tensorflow-1-2-keras-opencv3-python3-cuda8-and-cudnn5-1-324438dd46f0
Introduction to Statistics for Data Science https://medium.com/diogo-menezes-borges/introduction-to-statistics-for-data-science-16a188a400ca
When to use different machine learning algorithms: a simple guide https://medium.freecodecamp.org/when-to-use-different-machine-learning-algorithms-a-simple-guide-ba615b19fb3b
A Gentle Introduction to LSTM Autoencoders https://machinelearningmastery.com/lstm-autoencoders/
How to Develop a Deep Convolutional Neural Network From Scratch for Fashion MNIST Clothing Classification https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-fashion-mnist-clothing-classification/
Understanding AUC - ROC Curve – Towards Data Science https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5
Ridge and Lasso Regression: A Complete Guide with Python Scikit-Learn https://towardsdatascience.com/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b
Intro to XGBoost: Predicting Life Expectancy with Supervised Learning https://www.kdnuggets.com/2019/05/intro-xgboost-predicting-life-expectancy-supervised-learning.html
What deep learning really means | InfoWorld https://www.infoworld.com/article/3163130/what-deep-learning-really-means.html
Machine learning algorithms explained | InfoWorld https://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.amp.html
Extreme Rare Event Classification using Autoencoders in Keras https://towardsdatascience.com/extreme-rare-event-classification-using-autoencoders-in-keras-a565b386f098
How to Generate Prediction Intervals with Scikit-Learn and Python https://towardsdatascience.com/how-to-generate-prediction-intervals-with-scikit-learn-and-python-ab3899f992ed
How to Develop a Convolutional Neural Network From Scratch for MNIST Handwritten Digit Classification https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-from-scratch-for-mnist-handwritten-digit-classification/
Gradient Descent Derivation http://mccormickml.com/2014/03/04/gradient-descent-derivation/ https://medium.freecodecamp.org/an-overview-of-the-gradient-descent-algorithm-8645c9e4de1e
Machine Learning Workflow https://medium.com/datadriveninvestor/my-machine-learning-workflow-7576f7dbcef3
Machine LearningPlus https://www.machinelearningplus.com/predictive-modeling/how-naive-bayes-algorithm-works-with-example-and-full-code/#9buildingnaivebayesclassifierinpython
Visualising Filters and Feature Maps for Deep Learning https://towardsdatascience.com/visualising-filters-and-feature-maps-for-deep-learning-d814e13bd671
Which machine learning model to use? https://towardsdatascience.com/which-machine-learning-model-to-use-db5fdf37f3dd
Introduction to Machine learning: Top-down approach https://towardsdatascience.com/introduction-to-machine-learning-top-down-approach-8f40d3afa6d7
Transfer Learning with Keras and Deep Learning - PyImageSearch https://www.pyimagesearch.com/2019/05/20/transfer-learning-with-keras-and-deep-learning/
Understand Classification Performance Metrics – Becoming Human: Artificial Intelligence Magazine https://becominghuman.ai/understand-classification-performance-metrics-cad56f2da3aa
How to Implement VGG, Inception and ResNet Modules for Convolutional Neural Networks from Scratch
https://machinelearningmastery.com/how-to-implement-major-architecture-innovations-for-convolutional-neural-networks/
Bayesian modeling, Data Science, and Python https://twiecki.io/blog/2013/08/12/bayesian-glms-1/
Introduction to Bayesian Linear Regression https://towardsdatascience.com/introduction-to-bayesian-linear-regression-e66e60791ea7
Unsupervised deep learning for data interpolation – Aliaksei Mikhailiuk – Medium https://medium.com/@aliaksei.mikhailiuk/unsupervised-learning-for-data-interpolation-e259cf5dc957
How to Develop a Convolutional Neural Network to Classify Photos of Dogs and Cats (with 97% accuracy) https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-photos-of-dogs-and-cats/
The Complete Guide to Decision Trees https://towardsdatascience.com/the-complete-guide-to-decision-trees-28a4e3c7be14
Natural Language Processing: From Basics to using RNN and LSTM https://towardsdatascience.com/natural-language-processing-from-basics-to-using-rnn-and-lstm-ef6779e4ae66
Image Classification with Tensorflow 2.0 – Towards Data Science https://towardsdatascience.com/image-classification-with-tensorflow-2-0-7696e4aa5ca7
Understanding Linear Regression and The Need For Gradient Descent https://towardsdatascience.com/understanding-linear-regression-and-the-need-for-gradient-descent-2cc0f25763d5
Intermediate ML https://www.kaggle.com/learn/intermediate-machine-learning
time-series-analysis-with-pandas https://www.dataquest.io/blog/tutorial-time-series-analysis-with-pandas/