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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

Apple Unveils Next-Gen M4 Chip: Revolutionizing AI Capabilities for iPad Pro

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 08 May, 2024
3 min read

Apple has launched its latest innovation – the M4 chip, at its ‘Let Loose’ iPad event. This new development from Apple has sparked a wave of excitement and speculation within the tech community. The M4 chip promises unparalleled performance and efficiency, especially for the new iPad Pro, aiming to redefine mobile computing. Let’s explore the innovative architecture, industry impact, and ethical implications of this new chip.

Also Read: MM1: Everything you Need to Know About Apple’s AI Model

Apple Unveils Next-Gen M4 Chip: Revolutionizing AI Capabilities for iPad Pro

A Dive into the M4’s Architecture

The M4 chip boasts a revamped CPU architecture, balancing power and efficiency with performance and efficiency cores. The M4 comes packed with a new 10-core CPU and a 10-core GPU, offering dynamic caching, mesh shading, and ray tracing support. This not only ensures power but also efficiency, making it a game-changer for the iPad Pro’s performance.

With enhancements in machine learning accelerators and accelerated memory bandwidth, the M4 promises unparalleled processing power and energy efficiency. Apple’s focus on the 3nm process underscores its dedication to efficiency and performance. Moreover, the focus on AI is quite evident with the M4 boasting an impressive 38 trillion operations per second.

Also Read: Apple Introduces OpenELM: Open-Source AI Models for On-Device Processing

M4 Enhances the New iPad Pro

The new iPad Pro launched at Apple’s Let Loose event boasts a number of new features and updates. The Tandem OLED display support and AV1 hardware decode elevate its visual experiences and streaming capabilities. The advanced Media Engine empowers users with seamless multimedia editing and playback. However, concerns arise regarding the limitations of iPadOS in fully utilizing the M4’s potential, signaling a need for a more versatile operating system.’

Apple Unveils Next-Gen M4 Chip at the Let Loose 2024 Event

Controversies and Concerns

The unveiling has raised debates surrounding the M4’s release timing, RAM constraints, and comparisons with competitors. Questions have been raised about the rapid succession of the M4 after the M3, with concerns regarding its impact on consumer satisfaction and upgrade cycles. However, Apple’s track record of innovation and user-centric design instills confidence in the potential of the M4 chip. Additionally, ethical considerations regarding AI and the environmental sustainability of the M4 are also being looked into.

Also Read: Apple Launches ReALM Model that Outperforms GPT-4

Exploring the M4’s Impact

Looking ahead, the implications of the M4 chip are vast. From its potential integration into future MacBook models to its role in shaping the ARM revolution, it’s clear that the M4 is set to redefine mobile computing. As the Indian tech community eagerly anticipates its arrival, discussions abound regarding its potential applications and benefits.

Our Say

Amidst the debates and discussions surrounding the M4 chip, Apple’s commitment to pushing the boundaries of technology remains unwavering. Despite being promising, the true measure of the M4’s success lies in its real-world performance. With the M4 leading the charge, the future of mobile computing seems to have limitless possibilities to explore.

Follow us on Google News to stay updated with the latest innovations in the world of AI, Data Science, & GenAI.

AIAI chipAppleApple eventarchitectureArtificial IntelligenceefficiencyeventiPadiPad ProLet LooseM4 chip
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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