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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Discover One of the Fastest Sorting Algorithms with DeepMind’s AlphaDev

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 15 Jun, 2023
3 min read

Algorithms are the backbone of modern technology, driving everything from data analysis to optimization. They provide step-by-step instructions to computers and software, enabling efficient and consistent performance across various domains. Sorting and searching algorithms, in particular, are widely used by students and programmers alike. While human intuition has been instrumental in algorithm development, optimizing their performance has become increasingly challenging. Traditional methods and human scientists have reached their limits in further enhancing algorithm efficiency. However, a solution has emerged: DeepMind’s AlphaDev, an AI-based deep reinforcement learning agent with the potential to revolutionize algorithm design.

Learn More: Do check out our workshop at the DataHack Summit 2023, which unlocks the incredible potential of Reinforcement Learning and tackles real-world challenges using the latest AI techniques.

Google DeepMind has developed AlphaDev - an AI-based deep reinforcement learning agent set to revolutionize algorithm design.

The Power of Deepmind’s AlphaDev

To overcome the limitations of traditional algorithm-optimization techniques, DeepMind has harnessed the power of artificial intelligence, specifically deep reinforcement learning. AlphaDev is an innovative deep reinforcement learning agent trained to navigate vast search spaces. It is capable of uncovering previously undiscovered algorithms that outperform human-designed benchmarks. AlphaDev treats complex problems as single-player games. It leverages its ability to learn from experience and optimize performance, reshaping the way developers conceive and build algorithms.

Introducing AssemblyGame

At the heart of AlphaDev’s capabilities lies AssemblyGame, an engaging single-player game that challenges players to create new and efficient sorting algorithms by selecting low-level CPU instructions. The game’s immense search space and the nature of its reward function, where a single incorrect instruction can render the entire algorithm invalid, make it a formidable puzzle. To tackle this challenge, AlphaDev enters the stage. With its two core components—a learning algorithm and a representation function—AlphaDev combines deep reinforcement learning and stochastic search optimization algorithms. The primary learning algorithm used in AlphaDev is an extension of the renowned AlphaZero, a deep reinforcement learning algorithm.

Unleashing AlphaDev’s Potential

During its training process, AlphaDev achieved results by discovering small sorting algorithms from scratch that surpassed previous benchmarks set by human specialists. These newly unearthed algorithms have been integrated into the LLVM standard C++ sort library, effectively replacing a component with an algorithm generated automatically through reinforcement learning. This breakthrough signifies the adoption of an algorithm that outperforms human-designed approaches in terms of performance. Furthermore, AlphaDev’s versatility extends beyond sorting algorithms, demonstrating its potential to tackle a wide range of problem domains.

DeepMind's AlphaDev simplifies data sorting & analysis.

Unlocking the Future of Algorithm Design

In conclusion, AlphaDev represents a monumental leap forward in optimizing sorting algorithms and discovering efficient solutions through deep reinforcement learning and optimization techniques. By combining the power of AI with human ingenuity, DeepMind has unlocked a new era of algorithm design. AlphaDev’s ability to navigate complex search spaces and uncover superior algorithms has far-reaching implications, transcending the limitations of traditional methods. As the journey continues, the boundaries of algorithmic performance will be pushed further, leading to advancements in various domains. Get ready to embrace the future of algorithm design with AlphaDev at the helm.

Our Say

DeepMind’s AlphaDev is ushering in a new era of algorithm design, harnessing the potential of deep reinforcement learning to discover faster and more efficient sorting algorithms. With its capacity to learn from experience and optimize performance, AlphaDev challenges traditional approaches and reshapes the boundaries of algorithmic innovation. From AssemblyGame’s intriguing puzzles to AlphaDev’s integration into industry-standard libraries, this deep learning agent is already showing its impact. As we look ahead, the versatility & potential of AlphaDev offer exciting possibilities for solving a wide range of complex problems. Brace yourself for the algorithmic revolution driven by the ingenious collaboration of human intelligence and cutting-edge AI technology.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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