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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

Microsoft Unveils first AI chip: Maia 100 Chip and Cobalt CPU

NISHANT TIWARI
NISHANT TIWARI Last Updated : 17 Nov, 2023
3 min read

In a groundbreaking move at the annual Ignite conference, Microsoft unveiled the highly anticipated Maia 100 chip and Cobalt CPU. This strategic initiative aims to seize control of technology, establishing dominance in the fiercely competitive AI computing market. CEO Satya Nadella outlined ambitious plans to harness the power of these innovative chips.

Microsoft Maia Chip

Maia 100 Chip’s Debut

Microsoft’s Maia 100 chip, a homegrown artificial intelligence powerhouse, made its debut at Ignite. Currently undergoing testing with Bing and Office AI products, Maia 100 signals Microsoft’s intent to advance its AI capabilities. This chip promises to provide Azure cloud customers with a revolutionary platform for developing and running AI programs, offering a new dimension to content generation.

Microsoft Maia Chip

Cobalt CPU – A Game-Changer in Cloud Computing

Accompanying Maia 100 is the Cobalt CPU, an Arm-based processor meticulously designed for general-purpose compute workloads on the Microsoft Cloud. This dual reveal underscores Microsoft’s commitment to delivering end-to-end infrastructure systems, tailored from silicon choices to software, servers, racks, and cooling systems. The chips are slated to roll out early next year, initially powering services like Microsoft Copilot and Azure OpenAI Service.

Microsoft Maia 100 Chip and Cobalt CPU

Satya Nadella’s Vision for Maia

Addressing the audience at Ignite, Satya Nadella shared Microsoft’s vision for Maia. The chip will initially power Microsoft’s own AI applications before becoming accessible to partners and customers. Nadella emphasized the company’s dedication to delivering utmost efficiency, performance, and scale to users, highlighting the collaborative efforts with partners.

Azure’s Coordinated Hardware and Software Approach

Microsoft’s approach goes beyond unveiling chips; it involves a comprehensive strategy to optimize every layer of the infrastructure stack. Rani Borkar, Corporate Vice President for Azure Hardware Systems and Infrastructure, highlighted the importance of co-designing hardware and software to maximize performance and offer customers varied infrastructure choices. The announcement of Azure boost further reinforces Microsoft’s commitment to enhancing storage and networking speed.

Industry Partnerships and Ecosystem Expansion

In addition to Maia and Cobalt, Microsoft announced its collaboration with industry partners to broaden infrastructure options for customers. The introduction of new virtual machine series and GPU options demonstrates Microsoft’s dedication to providing diverse choices in terms of price and performance. The expansion of partnerships reflects the company’s customer-centric approach, ensuring they receive the best available options.

Microsoft’s Unique Silicon Journey

Microsoft’s journey into custom silicon is a strategic evolution. By designing its own chips, Microsoft gains control over every design choice, ensuring optimal performance for its cloud and AI workloads. The Maia 100 server boards and the unique cooling solution developed for these high-performance chips showcase Microsoft’s commitment to innovation and efficiency.

Our Say

Microsoft’s unveiling of the Maia 100 chip and Cobalt CPU marks a pivotal moment in the AI landscape. The company’s holistic approach, from chip design to datacenter infrastructure, signifies a dedication to providing cutting-edge solutions for its customers. As the tech giant continues to innovate, this announcement solidifies Microsoft’s position as a leader in shaping the future of AI and cloud computing. The Maia 100 and Cobalt CPU are not just chips; they represent Microsoft’s commitment to optimizing every layer of its technological stack for the benefit of its customers. Consequently, the future of Azure and AI has never looked more promising.

AIAzureCloudCPU
NISHANT TIWARI
NISHANT TIWARI

Seasoned AI enthusiast with a deep passion for the ever-evolving world of artificial intelligence. With a sharp eye for detail and a knack for translating complex concepts into accessible language, we are at the forefront of AI updates for you. Having covered AI breakthroughs, new LLM model launches, and expert opinions, we deliver insightful and engaging content that keeps readers informed and intrigued. With a finger on the pulse of AI research and innovation, we bring a fresh perspective to the dynamic field, allowing readers to stay up-to-date on the latest developments.

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