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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

How to get Text, Images, Videos… All in One using REKA CORE?

NISHANT TIWARI
NISHANT TIWARI Last Updated : 25 Apr, 2024
5 min read

Introduction

Imagine a world where Artificial Intelligence seamlessly comprehends not just text but also images, videos, and audio. Reka has launched Reka Core, a new frontier-class multimodal language model that supports input of text, image, video, and audio. This model is one of only two commercially available with such capabilities, offering advanced performance in automated base model evaluations and blind third-party human evaluations. Core is designed for agentic workflows and complex tasks requiring sophisticated analysis.

Table of contents

Key Features of Reka Core

Reka Core is a powerful multimodal language model that excels in processing text, images, videos, and audio inputs. It has been trained to understand and reason with diverse types of data, making it a versatile tool for various applications. The model’s ability to handle multiple modalities sets it apart from traditional language models. Also allowing it to provide comprehensive and contextually relevant responses across different media formats.

  • Multimodal Understanding: Reka Core is a versatile tool capable of processing and reasoning with text, images, videos, and audio inputs. Showcasing its proficiency in multimodal understanding, making it useful for various applications.
  • Powerful Performance: Reka Core outperforms leading models in benchmarks, delivering significant value for its compute class. It surpasses larger models in automatic and blind human evaluations.
Reka Core
  • Advanced Capabilities(128K Context Window, Reasoning, Coding, Multilingual Fluency): Reka Core, with a 128K context window, strong reasoning abilities, and multilingual fluency, outperforms benchmarks in long-context tasks and retrieval, with 25% of pretraining data code-related.
  • Flexible Deployment Options: Reka Core provides versatile deployment options for chat applications and developer platforms, catering to diverse user needs, making it a versatile solution for various applications.

Competitive Advantages

Reka Core offers affordable pricing for its API, making it accessible for widespread use. The Reka suite of multimodal language models, including Reka Core, Flash, and Edge, provides state-of-the-art performance at a competitive price point. The pricing of $10 per million input tokens and $25 per million output tokens is competitive with other industry peers, ensuring that customers can leverage Core’s capabilities without breaking the bank.

With the ability to outperform other models in their compute class, such as Gemma 7B and Mistral 7B, Reka Flash and Edge offer significant value for their scale. This cost-effective pricing strategy positions Reka Core as an attractive option for businesses and organizations looking to integrate advanced multimodal language models into their workflows. By providing accessible API costs, Reka Core aims to democratize access to multimodal technology and enable a broader range of use cases across various industries.

Reka’s Commitment to Multimodal Innovation

Reka demonstrates dedication to developing frontier multimodal models through the launch of Core, Edge, and Flash. In terms of fulfilling Reka’s objective to offer complete multimodal solutions for a variety of industries and use cases, these models mark a major advancement. The models in the suite facilitate the processing of text, image, video, and audio inputs, opening up more diverse and expansive consumer use cases in sectors including robotics, digital content creation, e-commerce, social media, and video games.

Strategic alliances that Reka has forged with top international technology platforms and governmental institutions are essential to its dedication to multimodal innovation. These alliances, which include ones with AI Singapore, Snowflake, and Oracle, are crucial to democratizing multimodal technology access. Through working together with these partners, Reka hopes to make it possible for businesses and individuals everywhere to use and develop with Reka models, which will accelerate the worldwide adoption of multimodal technology.

Getting Started with Reka Core

Here’s how you log in to the Reka Platform:

  1. Visit Reka’s Website

    Navigate to Reka Playground and you’ll see the the Log in and Try Reka option in middle of the screen. Click on Try Reka and it’ll take you to window to create your account.

  2. Agree to terms

    After logging in, agree to terms, then enter a chat-like interface similar to ChatGPT or Gemini.Reka core

Prompting Reka Core with a Video

  • I provided Reka a downloaded YouTube Video and asking it to identify the characters and describe the scene.
  • In few seconds you have a response, and I amazed by the output I recieved.
  • The output’s quality is impressive if we disregard the emotional nuances missed in the scene.

The Future of Multimodal AI with Reka Core

Reka Core, the latest addition to Reka’s leading multimodal models. It represents a significant leap forward in the field of AI technology. With its ability to process text, image, video, and audio input, Core is poised to revolutionize interaction with AI systems. The launch of Core marks a pivotal moment in the development of frontier multimodal models. It positions Reka as a key player in the advancement of AI capabilities.

Open-Source Collaboration and Advancing AI Capabilities

In its commitment to advancing AI, Reka actively engages in open-source collaboration, enhancing its multimodal models’ capabilities. Collaborating with leading global tech platforms like Snowflake, Oracle, and AI Singapore, Reka has democratized multimodal technology access. This has allowed businesses and individuals all across the world to take advantage of its state-of-the-art AI models.

Partnering for the Future of Multimodal AI

Reka’s vision for multimodal AI emphasizes partnership and strategic collaboration. They invite new collaborators to realize its full potential, benefiting all people through the use of diverse stakeholders’ knowledge.

Conclusion

In the development of multimodal AI technology, the release of Reka Core marks a critical turning point. Core is positioned to be a disruptive force in the AI space. Thanks to its unmatched skills to analyze multimodal input. Its competitive performance across important widely recognized evaluation measures.
The future of multimodal AI is promising as Reka continues to innovate and influence the field.

You can also read our more articles related to AI tools here.

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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