Best Object Oriented Podcasts (2024)

1
Scala: A Modern Language for Functional and Object-Oriented Programming 5:51

1h ago5:51

5:51

Scala, short for "scalable language," is a powerful programming language that merges the best features of both object-oriented and functional programming paradigms. Designed to be concise, elegant, and expressive, Scala offers a robust framework for developers to build scalable and maintainable software solutions. Combining Paradigms One of Scala's…

1
RL4J: Empowering Reinforcement Learning in Java 4:40

34m ago4:40

4:40

RL4J is a powerful open-source library designed for reinforcement learning (RL) applications within the Java ecosystem. Developed as part of the Deeplearning4j project, RL4J aims to provide developers and researchers with robust tools to implement and experiment with various reinforcement learning algorithms. As machine learning continues to expand…

1
Arbiter: Streamlining Optimization and Hyperparameter Tuning for Machine Learning Models 4:20

25m ago4:20

4:20

Arbiter is an advanced tool designed to enhance the process of optimization and hyperparameter tuning in machine learning models. As machine learning continues to evolve, the importance of fine-tuning model parameters to achieve optimal performance has become increasingly critical. Key Features of Arbiter Automated Hyperparameter Tuning: Arbiter au…

1
Hypothesis Testing: A Guide to Z-Test, T-Test, and ANOVA 49:12

3d ago49:12

49:12

Hypothesis testing is a fundamental method in statistics used to make inferences about a population based on sample data. It provides a structured approach to evaluate whether observed data deviates significantly from what is expected under a specific hypothesis. Three commonly used hypothesis tests are the Z-test, T-test, and ANOVA, each serving d…

1
Non-parametric Tests: Flexible Tools for Statistical Analysis 5:12

2h ago5:12

5:12

Non-parametric tests are a class of statistical methods that do not rely on assumptions about the underlying distribution of data. Unlike parametric tests, which assume a specific distribution for the data, non-parametric tests are more flexible and can be applied to a wider range of data types. This makes them particularly useful in situations whe…

1
We just go however long now... 2:18:18

1h ago2:18:18

2:18:18

Darkmode VSOPBy Daniel Coulbourne & Caleb Porzio

1
General Linear Model (GLM): A Versatile Framework for Data Analysis 3:55

7h ago3:55

3:55

The General Linear Model (GLM) is a foundational framework in statistical analysis, widely used for modeling and understanding relationships between variables. It offers a flexible and comprehensive approach for analyzing data by encompassing various types of linear relationships and can be applied across numerous fields including economics, social…

1
Statistical Models: Frameworks for Understanding and Predicting Data 4:42

1h ago4:42

4:42

Statistical models are powerful tools that allow us to understand, describe, and predict patterns in data. These models provide a structured way to capture the underlying relationships between variables, enabling us to make informed decisions, test hypotheses, and generate predictions about future outcomes. Whether in science, economics, medicine, …

1
Point and Interval Estimation: Tools for Accurate Statistical Inference 5:36

30m ago5:36

5:36

Point and interval estimation are key concepts in statistics that provide methods for estimating population parameters based on sample data. These techniques are fundamental to making informed decisions and predictions in various fields, from science and engineering to economics and public policy. By offering both specific values and ranges of plau…

1
P-values and Confidence Intervals: Essential Tools for Statistical Decision-Making 3:52

2h ago3:52

3:52

P-values and confidence intervals are fundamental concepts in statistical analysis, providing critical insights into the reliability and significance of data findings. These tools help researchers, scientists, and analysts make informed decisions based on sample data, enabling them to draw conclusions about broader populations with a known level of…

1
ImageNet: Revolutionizing Computer Vision and Deep Learning 37:22

5d ago37:22

37:22

ImageNet is a large-scale visual database designed for use in visual object recognition research, and it has played a pivotal role in advancing the field of computer vision and deep learning. Launched in 2009 by researchers at Princeton and Stanford, ImageNet consists of millions of labeled images categorized into thousands of object classes, makin…

1
Bayesian Inference and Posterior Distributions: A Dynamic Approach to Statistical Analysis 4:36

2h ago4:36

4:36

Bayesian inference is a powerful statistical method that provides a framework for updating our beliefs in light of new evidence. Rooted in Bayes' theorem, this approach allows us to combine prior knowledge with new data to form updated, or posterior, distributions, which offer a more nuanced and flexible understanding of the parameters we are study…

1
Statistical Inference: Drawing Conclusions from Data 5:45

10h ago5:45

5:45

Statistical inference is a critical branch of statistics that involves making predictions, estimates, or decisions about a population based on a sample of data. It serves as the bridge between raw data and meaningful insights, allowing researchers, analysts, and decision-makers to draw conclusions that extend beyond the immediate data at hand. Core…

1
Sampling Techniques: Ensuring Representativeness in Data Collection 3:33

1d ago3:33

3:33

Sampling techniques are crucial methods used in statistics to select a subset of individuals or observations from a larger population. These techniques allow researchers to gather data efficiently while ensuring that the sample accurately reflects the characteristics of the entire population. Among the most widely used sampling methods are random s…

1
Sampling Distributions: The Bridge Between Sample Data and Population Insights 5:42

3d ago5:42

5:42

Sampling distributions are a fundamental concept in statistics that play a crucial role in understanding how sample data relates to the broader population. When we collect data from a sample, we often want to make inferences about the entire population from which the sample was drawn. However, individual samples can vary, leading to differences bet…

1
Central Limit Theorem (CLT): The Pillar of Statistical Inference 5:50

14d ago5:50

5:50

The Central Limit Theorem (CLT) is one of the most important and foundational concepts in statistics. It provides a crucial link between probability theory and statistical inference, enabling statisticians and researchers to draw reliable conclusions about a population based on sample data. The CLT states that, under certain conditions, the distrib…

1
Sampling and Distributions: The Cornerstones of Statistical Analysis 3:31

11d ago3:31

3:31

Sampling and distributions are fundamental concepts in statistics that play a crucial role in analyzing and understanding data. They form the backbone of statistical inference, enabling researchers to draw conclusions about a population based on a smaller, manageable subset of data. By understanding how samples relate to distributions, statistician…

1
Kernel Density Estimation (KDE): A Powerful Technique for Understanding Data Distributions 3:41

7d ago3:41

3:41

Kernel Density Estimation (KDE) is a non-parametric method used in statistics to estimate the probability density function of a random variable. Unlike traditional methods that rely on predefined distributions, KDE provides a flexible way to model the underlying distribution of data without making strong assumptions. This makes KDE a versatile and …

1
Distribution-Free Tests: Flexible Approaches to Hypothesis Testing Without Assumptions 3:02

7d ago3:02

3:02

Distribution-free tests, also known as non-parametric tests, are statistical methods used for hypothesis testing that do not rely on any assumptions about the underlying distribution of the data. Unlike parametric tests, which assume that data follows a specific distribution (such as the normal distribution), distribution-free tests offer a more fl…

1
Non-Parametric Statistics: Flexible Tools for Analyzing Data Without Assumptions 3:58

8d ago3:58

3:58

Non-parametric statistics is a branch of statistics that offers powerful tools for analyzing data without the need for making assumptions about the underlying distribution of the data. Unlike parametric methods, which require the data to follow a specific distribution (such as the normal distribution), non-parametric methods are more flexible and c…

1
Factor Analysis (FA): Unveiling Hidden Structures in Complex Data 4:00

9d ago4:00

4:00

Factor Analysis (FA) is a statistical method used to identify underlying relationships between observed variables. By reducing a large set of variables into a smaller number of factors, FA helps to simplify data, uncover hidden patterns, and reveal the underlying structure of complex datasets. This technique is widely employed in fields such as psy…

1
No Revenue. No Agency. 1:22:46

9d ago1:22:46

1:22:46

Kids have it the worst, dude.By Daniel Coulbourne & Caleb Porzio

1
Probability Distributions: A Fundamental Tool for Understanding Uncertainty 3:45

10d ago3:45

3:45

Probability distributions are essential concepts in statistics and probability theory, providing a way to describe how probabilities are spread across different outcomes of a random event. They are the foundation for analyzing and interpreting data in various fields, enabling us to understand the likelihood of different outcomes, assess risks, and …

1
Probability Distributions: Mapping the Likelihood of Outcomes 4:30

11d ago4:30

4:30

Probability distributions are fundamental concepts in statistics and probability theory that describe how the probabilities of different possible outcomes are distributed across a range of values. By providing a mathematical description of the likelihood of various outcomes, probability distributions serve as the backbone for understanding and anal…

1
Things to Know when Considering Multi-Tenant or Multi-Threaded Applications 1:58:45

11d ago1:58:45

1:58:45

For the full show notes head over to: https://www.codingblocks.net/episode241By Michael Outlaw, Allen Underwood, Joe Zack

1
Probability Spaces: The Foundation of Modern Probability Theory 3:36

12d ago3:36

3:36

Probability spaces form the fundamental framework within which probability theory operates. They provide a structured way to describe and analyze random events, offering a mathematical foundation for understanding uncertainty, risk, and randomness. By defining a space where all possible outcomes of an experiment or random process are considered, pr…

1
Multivariate Statistics: Analyzing Complex Data with Multiple Variables 4:35

13d ago4:35

4:35

Multivariate statistics is a branch of statistics that deals with the simultaneous observation and analysis of more than one statistical outcome variable. Unlike univariate or bivariate analysis, which focus on one or two variables at a time, multivariate statistics considers the interrelationships between multiple variables, providing a more compr…

1
Graph Recurrent Networks (GRNs): Bridging Temporal Dynamics and Graph Structures 4:43

14d ago4:43

4:43

Graph Recurrent Networks (GRNs) are an advanced type of neural network that combines the capabilities of recurrent neural networks (RNNs) with graph neural networks (GNNs) to model data that is both sequential and structured as graphs. GRNs are particularly powerful in scenarios where the data not only changes over time but is also interrelated in …

1
Ruby: A Dynamic, Elegant Programming Language for Web Development and Beyond 3:44

15d ago3:44

3:44

Ruby is a dynamic, open-source programming language known for its simplicity, elegance, and productivity. Created by Yukihiro "Matz" Matsumoto in the mid-1990s, Ruby was designed with the principle of making programming both enjoyable and efficient. The language’s intuitive syntax and flexibility make it a favorite among developers, especially for …

1
Vue.js: The Progressive JavaScript Framework for Modern Web Applications 6:59

16d ago6:59

6:59

Vue.js is an open-source JavaScript framework used for building user interfaces and single-page applications. Created by Evan You in 2014, Vue.js has quickly gained popularity among developers for its simplicity, flexibility, and powerful features. It is designed to be incrementally adoptable, meaning that it can be used for everything from enhanci…

1
ReactJS: A Powerful Library for Building Dynamic User Interfaces 5:09

17d ago5:09

5:09

ReactJS is a popular open-source JavaScript library used for building user interfaces, particularly single-page applications where a seamless user experience is key. Developed and maintained by Facebook, ReactJS has become a cornerstone of modern web development, enabling developers to create complex, interactive, and high-performance user interfac…

1
Apache Spark: The Unified Analytics Engine for Big Data Processing 29:04

23d ago29:04

29:04

Apache Spark is an open-source, distributed computing system designed for fast and flexible large-scale data processing. Originally developed at UC Berkeley’s AMPLab, Spark has become one of the most popular big data frameworks, known for its ability to process vast amounts of data quickly and efficiently. Spark provides a unified analytics engine …

1
Clojure: A Dynamic, Functional Programming Language for the JVM 3:43

23d ago3:43

3:43

Clojure is a modern, dynamic, and functional programming language that runs on the Java Virtual Machine (JVM). Created by Rich Hickey in 2007, Clojure is designed to be simple, expressive, and highly efficient for concurrent programming. It combines the powerful features of Lisp, a long-standing family of programming languages known for its flexibi…

1
Caffe: A Deep Learning Framework for Speed and Modularity 3:18

20d ago3:18

3:18

Caffe is an open-source deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and contributed to by a global community of researchers and engineers. Designed with an emphasis on speed, modularity, and ease of use, Caffe is particularly well-suited for developing and deploying deep learning models, especially in the fie…

1
Nimfa: A Python Library for Non-negative Matrix Factorization 5:43

21d ago5:43

5:43

Nimfa is a Python library specifically designed for performing Non-negative Matrix Factorization (NMF), a powerful technique used in data analysis to uncover hidden structures and patterns in non-negative data. Developed to be both flexible and easy to use, Nimfa provides a comprehensive set of tools for implementing various NMF algorithms, making …

1
FastAPI: High-Performance Web Framework for Modern APIs 6:30

28d ago6:30

6:30

FastAPI is a modern, open-source web framework for building APIs with Python. Created by Sebastián Ramírez, FastAPI is designed to provide high performance, easy-to-use features, and robust documentation. It leverages Python's type hints to offer automatic data validation and serialization, making it an excellent choice for developing RESTful APIs …

1
NetBeans: A Comprehensive Integrated Development Environment 4:23

29d ago4:23

4:23

NetBeans is a powerful, open-source integrated development environment (IDE) used by developers to create applications in various programming languages. Initially developed by Sun Microsystems and now maintained by the Apache Software Foundation, NetBeans provides a robust platform for building desktop, web, and mobile applications. It supports a w…

1
Area Under the Curve (AUC): A Comprehensive Metric for Evaluating Classifier Performance 5:24

26d ago5:24

5:24

The Area Under the Curve (AUC) is a widely used metric in the evaluation of binary classification models. It provides a single scalar value that summarizes the performance of a classifier across all possible threshold values, offering a clear and intuitive measure of how well the model distinguishes between positive and negative classes. The AUC is…

1
Non-Negative Matrix Factorization (NMF): Uncovering Hidden Patterns in Data 7:43

27d ago7:43

7:43

Non-Negative Matrix Factorization (NMF) is a powerful technique in the field of data analysis and machine learning used to reduce the dimensionality of data and uncover hidden patterns. Unlike other matrix factorization methods, NMF imposes the constraint that the matrix elements must be non-negative. This constraint makes NMF particularly useful f…

1
Two Water Coolers Walk Into a Bar 1:33:43

25d ago1:33:43

1:33:43

Grab your headphones because it's water cooler time! In this episode we're catching up on feedback, putting our skills to the test, and wondering what we're missing. Plus, Allen's telling it how it is, Outlaw is putting it all together and Joe is minding the gaps! View the full show notes here: https://www.codingblocks.net/episode240 Reviews Thank …

1
Signal Detection Theory (SDT): Understanding Decision-Making in the Presence of Uncertainty 4:51

28d ago4:51

4:51

Signal Detection Theory (SDT) is a framework used to analyze and understand decision-making processes in situations where there is uncertainty. Originating in the fields of radar and telecommunications during World War II, SDT has since been applied across various domains, including psychology, neuroscience, medical diagnostics, and market research…

1
AngularJS: Revolutionizing Web Development with Dynamic Applications 5:57

29d ago5:57

5:57

AngularJS is a powerful JavaScript-based open-source front-end web framework developed by Google. Introduced in 2010, AngularJS was designed to simplify the development and testing of single-page applications (SPAs) by providing a robust framework for client-side model–view–controller (MVC) architecture. It has significantly transformed the way dev…

1
Expectation-Maximization Algorithm (EM): A Powerful Tool for Data Analysis 4:24

1M ago4:24

4:24

The Expectation-Maximization (EM) algorithm is a widely-used statistical technique for finding maximum likelihood estimates in the presence of latent variables. Developed by Arthur Dempster, Nan Laird, and Donald Rubin in 1977, the EM algorithm provides an iterative method to handle incomplete data or missing values, making it a cornerstone in fiel…

1
Kotlin: A Modern Programming Language for the JVM and Beyond 7:05

1M ago7:05

7:05

Kotlin is a contemporary programming language developed by JetBrains, designed to be fully interoperable with Java while offering a more concise and expressive syntax. Introduced in 2011 and officially released in 2016, Kotlin has rapidly gained popularity among developers for its modern features, safety enhancements, and seamless integration with …

1
Dynamic Topic Models (DTM): Capturing the Evolution of Themes Over Time 5:00

1M ago5:00

5:00

Dynamic Topic Models (DTM) are an advanced extension of topic modeling techniques designed to analyze and understand how topics in a collection of documents evolve over time. Developed to address the limitations of static topic models like Latent Dirichlet Allocation (LDA), DTMs allow researchers and analysts to track the progression and transforma…

1
False Positive Rate (FPR): A Critical Metric for Evaluating Classification Accuracy 6:14

1M ago6:14

6:14

The False Positive Rate (FPR) is a crucial metric used to evaluate the performance of binary classification models. It measures the proportion of negative instances that are incorrectly classified as positive by the model. Understanding FPR is essential for assessing how well a model distinguishes between classes, particularly in applications where…

1
We Can't Talk About Anything 1:42:30

1M ago1:42:30

1:42:30

Things are in full swing. There are many things we cannot say. We are both slammed trying to get everything done for Laracon. Which led to this not being put out very quickly.By Daniel Coulbourne & Caleb Porzio

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