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Reading list

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
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 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

AV Bytes: AI Breakthroughs Featuring FLUX.1, Gemma 2, SAM 2 and More

Aayush1
Aayush1 03 Aug, 2024
4 min read

Introduction

Welcome back to AV Bytes, your weekly pit stop in the fast-paced world of AI! This week, we’re unpacking some impressive innovations that are turning heads in the tech sphere. Black Forest Labs’ FLUX.1 is giving Midjourney a run for its money in the text-to-image race, while Google DeepMind’s Gemma 2 is proving that good things come in small packages. Not to be outdone, Meta’s SAM 2 is making video and image segmentation look like child’s play.

But it’s not all fun and games in the AI playground. We’re also exploring how AI is flexing its muscles in the real world, from JPMorgan’s new research buddy to AI’s growing role in medical diagnostics. So grab your favorite beverage, settle in, and let’s take a friendly stroll through this week’s AI breakthroughs.

Overview

  • FLUX.1 Outshines Competitors: Black Forest Labs’ FLUX.1 excels in hyperrealistic text-to-image generation.
  • Gemma 2 Sets New Standards: Google DeepMind’s Gemma 2 outperforms larger models with 2 billion parameters.
  • SAM 2 Boosts Segmentation Speed: Meta’s SAM 2 enhances video and image segmentation efficiency.
  • JPMorgan’s AI Chatbot: AI chatbot streamlines research analysis in financial services.
  • Diffusion Augmented Agents: Google DeepMind introduces adaptable AI agents for complex tasks.
  • AI in Medical Diagnostics: AI detects prostate cancer more accurately than doctors.
  • Faster Ternary Inference: New technique doubles AI model inference speed on everyday computers.
  • Open-Source AI Support: US Department of Commerce endorses open-weight AI models.
  • AI in Coding Tools: Current AI coding tools show limited productivity improvements.
  • Privacy Concerns Rise: 74% of Americans worry about AI’s impact on privacy.

AI Model Innovations (FLUX.1, Gemma 2, SAM 2)

FLUX.1: A New Era in Text-to-Image Generation

FLUX.1, has taken the AI community by storm. Developed by Black Forest Labs, this model excels in generating hyperrealistic, fantastical, and photorealistic images from text prompts. FLUX.1 comes in three variants: Pro (API only), Dev (open-weight, non-commercial), and Schnell (Apache 2.0). All three variants outperform competitors like Midjourney and Ideogram, according to Black Forest Labs’ ELO score. The team also announced plans to develop state-of-the-art text-to-video models, marking one of the most confident model lab launches this year.

FLUX.1

Gemma 2 Release and AI Model Developments

Google DeepMind’s release of Gemma 2 marks a new benchmark in AI model performance, setting new standards with its impressive capabilities. The Gemma-2 2B model, featuring 2 billion parameters, achieved a score of 1130 on the Chatbot Arena, outperforming models ten times its size, such as GPT-3.5-Turbo-0613 and Mixtral-8x7b. This release also includes ShieldGemma, a safety classifier designed to detect harmful content, and Gemma Scope, which utilizes sparse autoencoders to analyze the model’s internal decision-making. These advancements highlight Google’s commitment to responsible AI development and have sparked discussions about AI model benchmarks and comparisons. However, there has been some criticism of the Human Eval Leaderboard for not accurately representing model performance. Overall, the Gemma 2 release underscores Google’s leadership in AI and its dedication to advancing technology responsibly.

Gemma 2

Meta’s Segment Anything Model 2 (SAM 2)

Meta has released SAM 2, a significant upgrade for video and image segmentation. SAM 2 operates at 44 frames per second for video segmentation, requires fewer interactions, and provides an 8.4 times speed improvement in video annotation over manual methods.

The model is available under Apache 2.0 license and comes with a new SA-V dataset that is 4.5x larger and has ~53x more annotations than the largest existing video segmentation dataset.

AI Research and Development

JPMorgan’s In-House AI Chatbot for Research Analysis

JPMorgan has introduced an in-house AI chatbot designed to assist with research analysis. This development highlights the growing trend of integrating AI into financial services to enhance efficiency and accuracy in data analysis.

The chatbot aims to streamline research processes, providing analysts with quick and accurate insights, thereby improving decision-making and productivity.

Diffusion Augmented Agents by Google DeepMind

Google DeepMind has introduced Diffusion Augmented Agents, a new approach that could revolutionize AI capabilities in complex environments. This research aims to enhance the adaptability and efficiency of AI agents, making them more capable of handling real-world tasks.

AI Outperforms Doctors in Prostate Cancer Detection

A recent study has shown that AI can detect prostate cancer 17% more accurately than doctors. This breakthrough underscores the potential of AI in medical diagnostics, offering a glimpse into a future where AI plays a crucial role in healthcare.

Faster Ternary Inference for AI Models

A new technique using AVX2 instructions has achieved a 2x speed boost in ternary model inference compared to Q8_0, without the need for custom hardware. This advancement allows larger AI models to run efficiently on everyday computers, making high-performance AI more accessible.

Open-source AI and Government Stance

The United States Department of Commerce has issued policy recommendations supporting the availability of key components of powerful AI models, endorsing “open-weight” models. This move has been praised by industry leaders and could influence future AI regulations and policies.

AI in Coding and Development

Despite the hype, current AI coding tools like Cursor, ChatGPT, and Claude have not significantly improved productivity in writing code. However, the potential of “passive AI” tools that work in the background, offering recommendations and identifying issues in code, is being explored.

AI and Privacy Concerns

A Yahoo Finance article reports that 74% of Americans fear AI will destroy privacy, highlighting growing public concern about AI’s impact on personal data protection. This sentiment underscores the need for robust AI ethics and privacy policies.

Our Say

The rapid advancements in AI technology continue to push the boundaries of what is possible. From groundbreaking model releases to significant research developments, the AI landscape is evolving at an unprecedented pace. As we navigate this exciting frontier, it is crucial to balance innovation with ethical considerations, ensuring that AI benefits society as a whole. Stay tuned to The AI Times for more updates on the ever-evolving world of artificial intelligence.

Follow us on Google News for next week’s update as we track the latest developments in the AI landscape.

Aayush1
Aayush1 03 Aug, 2024

Data Analyst with over 2 years of experience in leveraging data insights to drive informed decisions. Passionate about solving complex problems and exploring new trends in analytics. When not diving deep into data, I enjoy playing chess, singing, and writing shayari.

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