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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

DeepMind’s AI Master Gamer: Learns 26 Games in 2 Hours

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 21 Jun, 2023
3 min read

Reinforcement learning, a core research area of Google DeepMind, holds immense potential for solving real-world problems using AI. However, its training data and computing power inefficiency has posed significant challenges. DeepMind, in collaboration with researchers from Mila and Université de Montréal, has introduced an AI agent that defies these limitations. This agent, known as the Bigger, Better, Faster (BBF) model, has achieved superhuman performance on Atari benchmarks while learning 26 games in just two hours. This remarkable achievement opens new doors for efficient AI training methods and unlocks possibilities for future advancements in RL algorithms.

Learn More: Unlock the incredible potential of Reinforcement Learning and tackle real-world challenges using the latest AI techniques in our workshop at the DataHack Summit 2023.

The Efficiency Challenge of Reinforcement Learning

Reinforcement learning has long been recognized as a promising approach for enabling AI to tackle complex tasks. However, traditional RL algorithms suffer from inefficiencies that hamper their practical implementation. These algorithms demand extensive training data and substantial computing power, making them resource-intensive and time-consuming.

Also Read: A Comprehensive Guide to Reinforcement Learning

Google DeepMind builds Bigger, Better, Faster (BBF) AI model using reinforcement learning algorithms.

The Bigger, Better, Faster (BBF) Model: Outperforming Humans

DeepMind’s latest breakthrough comes from the BBF model, which has demonstrated exceptional performance on Atari benchmarks. While previous RL agents have surpassed human players in Atari games, what sets BBF apart is its ability to achieve such impressive results within a mere two hours of gameplay—a timeframe equivalent to that available to human testers.

Model-Free Learning: A New Approach

The success of BBF can be attributed to its unique model-free learning approach. By relying on rewards and punishments received through interactions with the game world, BBF bypasses the need to construct an explicit game model. This streamlined process lets the agent focus solely on learning and optimizing its performance, resulting in faster and more efficient training.

Also Read: Enhancing Reinforcement Learning with Human Feedback using OpenAI and TensorFlow

Google DeepMind's Bigger, Better, Faster (BBF) AI model is trained using the rewards and punishments method

Enhanced Training Methods and Computational Efficiency

BBF’s rapid learning achievement is the result of several key factors. The research team employed a larger neural network, refined self-monitoring training methods, and implemented various techniques to enhance efficiency. Notably, BBF can be trained on a single Nvidia A100 GPU, reducing the computational resources required compared to previous approaches.

Benchmarking Progress: A Stepping Stone for RL Advancements

Although BBF has not yet surpassed human performance across all games in the benchmark, it outshines other models in terms of efficiency. When compared to systems trained on 500 times more data across all 55 games, BBF’s efficient algorithm demonstrates comparable performance. This outcome validates the Atari benchmark’s suitability and provides encouragement to smaller research teams seeking funding for their RL projects.

Google DeepMind's reinforcement learning BBF model has outperformed humans at playing games.

Beyond Atari: Expanding the Frontier of RL

While the BBF model’s success has been demonstrated on Atari games, its implications extend beyond this specific domain. The efficient learning techniques and breakthroughs achieved with BBF pave the way for further advancements in reinforcement learning. By inspiring researchers to push the boundaries of sample efficiency in deep RL, the goal of achieving human-level performance with superhuman efficiency across all tasks becomes increasingly feasible.

Also Read: Researches Suggest Prompting Framework Which Outperforms Reinforcement Learning

Implications for the AI Landscape: A Step Towards Balance

The emergence of more efficient RL algorithms, such as BBF, serves as a vital step toward establishing a balanced AI landscape. While self-supervised models have dominated the field, the efficiency and effectiveness of RL algorithms can offer a compelling alternative. DeepMind’s achievement with BBF sparks hopes for a future where RL can play a significant role in addressing complex real-world challenges through AI.

Our Say

DeepMind’s development of the BBF model, capable of learning 26 games in just two hours, marks a significant milestone in reinforcement learning. By introducing a model-free learning algorithm and leveraging enhanced training methods, DeepMind has revolutionized the efficiency of RL. This breakthrough propels the field forward and inspires researchers to continue pushing the boundaries of sample efficiency. The future is aiming for human-level performance with unparalleled efficiency across all tasks.

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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