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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 RoboCat: A Self-Learning Robotic AI Model

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 26 Jun, 2023
3 min read

DeepMind, the renowned AI research lab, has unveiled its AI model named RoboCat, capable of performing a wide range of complex tasks using various models of robotic arms. Unlike previous models, RoboCat stands out for its ability to solve multiple tasks and adapt seamlessly to different real-world robots. Let’s delve into the details of this remarkable achievement & explore how RoboCat is set to revolutionize the field of robotics.

Also Read: Amazon’s Secret AI Robot for Home Can Do Everything and More

DeepMind's latest AI model, RoboCat, can perform a wide range of complex real-world tasks using various models of robotic arms.

The Versatile RoboCat: A Leap in Robotic Intelligence

DeepMind’s breakthrough AI model, RoboCat, showcases an unprecedented level of versatility in robotics. As stated by Alex Lee, a research scientist at DeepMind, RoboCat is a single large model capable of tackling diverse tasks across multiple real robotic embodiments. This means that the model can quickly adapt to new tasks and different robot configurations. This marks a significant milestone in the field of robotics.

Also Read: Jizai Arms – AI Robotic Arms That Turns You Into Spider-Man

DeepMind's AI Robot, RoboCat, performing real-world, simulated, and self-generated movements

Inspired by Gato: From Text to Robotics

RoboCat draws inspiration from GATO, another AI model developed by DeepMind. GATO possesses the remarkable ability to analyze and respond to text, images, and events. By leveraging this concept, DeepMind’s researchers trained RoboCat on a vast dataset. This comprised images and action data collected from both simulated and real-life robotics environments.

Training the Mighty RoboCat

To train RoboCat, the team at DeepMind gathered 100 – 1,000 demonstrations of various tasks performed by a human-controlled robotic arm. These demonstrations served as the foundation for fine-tuning the model on specific tasks, creating specialized “spin-off” models. Each spin-off model underwent rigorous practice, averaging 10,000 iterations on the respective task.

Also Read: World’s First AI-Powered Arm: All You Need to Know

RoboCat's robotic arms can perform a wide range of complex real-world tasks.

Pushing the Limits: Unleashing RoboCat’s Potential

The final version of RoboCat was trained on an impressive total of 253 tasks and benchmarked against 141 variations of those tasks, encompassing both simulated and real-world scenarios. DeepMind reports that the model successfully learned to operate different robotic arms after observing 1,000 human-controlled demonstrations over several hours. However, the success rates varied significantly, ranging from 13% to 99% across different tasks, with the number of demonstrations being a determining factor.

Also Read: Alphabet Unleashes Flowstate: Robotic App Development Platform for Everyone

Training process of DeepMind's self-improving AI Model RoboCat.

Unlocking New Frontiers: Redefining Robotics

Despite the varying success rates, DeepMind believes that RoboCat has the potential to lower the barriers to solving new tasks in robotics. Alex Lee explains that even with a limited number of demonstrations for a new task, RoboCat can be fine-tuned and generate additional data to further enhance its performance. The ultimate goal is to reduce the number of demonstrations required to teach RoboCat a new task to fewer than 10, which could revolutionize the field of robotics.

Also Read: Meet Phoenix Robot of Sanctuary AI and Tesla’s Latest Launch, Optimus!

Our Say

DeepMind’s RoboCat represents a significant breakthrough in robotics. It showcases the ability of a single AI model to adapt and excel across multiple tasks and different robot embodiments. By leveraging its training on a vast dataset and harnessing the power of fine-tuning, RoboCat has laid the foundation for future advancements in the field. With the potential to streamline the process of teaching robots new tasks, RoboCat may herald a new era of innovation. Exciting times lie ahead as RoboCat paves the way for a future where robots can seamlessly adapt and learn with minimal human intervention.

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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