Bästa Deep Learning podcaster (2024)

1
Wise Worldview Formation 26:40

1d ago26:40

26:40

Worldview is the key grid through which we filter and formulate ideas, yet it is not systematically developed in most educational programs. This is particularly likely on the university level. Make no mistake—a worldview is being developed anyway, but likely full of flaws and non sequiturs.Av Michael Gray

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Does the DIFF Transformer make a Diff? 8:03

15d ago8:03

8:03

Introducing a novel transformer architecture, Differential Transformer, designed to improve the performance of large language models. The key innovation lies in its differential attention mechanism, which calculates attention scores as the difference between two separate softmax attention maps. This subtraction effectively cancels out irrelevant co…

1
Preschool Pedagogy is Primary 30:52

15d ago30:52

30:52

No one wants to kill the joy of learning in a young child, but that's likely with the majority of preschool and elementary pedagogies. This podcast helps you sort through the educational philosophy underlying some major options.Av Michael Gray

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Automating Scientific Discovery: ScienceAgentBench 9:49

16d ago9:49

9:49

Introducing, ScienceAgentBench, a new benchmark for evaluating language agents designed to automate scientific discovery. The benchmark comprises 102 tasks extracted from 44 peer-reviewed publications across four disciplines, encompassing essential tasks in a data-driven scientific workflow such as model development, data analysis, and visualizatio…

1
Prune This! PyTorch and Efficient AI 8:04

16d ago8:04

8:04

Both sources explain neural network pruning techniques in PyTorch. The first source, "How to Prune Neural Networks with PyTorch," provides a general overview of the pruning concept and its various methods, along with practical examples of how to implement different pruning techniques using PyTorch's built-in functions. The second source, "Pruning T…

1
AlexWho? Going Deeper with Deep CNNs 11:50

18d ago11:50

11:50

The source is a chapter from the book "Dive into Deep Learning" that explores the historical development of deep convolutional neural networks (CNNs), focusing on the foundational AlexNet architecture. The authors explain the challenges faced in training CNNs before the advent of AlexNet, including limited computing power, small datasets, and lack …

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Predicting the Future from the Past: Sequential RNN Stuff 9:47

19d ago9:47

9:47

This text is an excerpt from the "Dive into Deep Learning" book, specifically focusing on the processing of sequential data. The authors introduce the challenges of working with data that occurs in a specific order, like time series or text, and how these sequences cannot be treated as independent observations. They delve into autoregressive models…

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Google's Secrets to Getting People to Adopt A.I. 8:39

20d ago8:39

8:39

This excerpt from "Mental Models," a chapter in the "People + AI Guidebook," focuses on the importance of understanding and managing user mental models when designing AI-powered products. The authors discuss how to set expectations for adaptation, onboard users in stages, plan for co-learning, and account for user expectations of human-like interac…

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LLM Tokenizers, from HFs LNP Course 12:23

23d ago12:23

12:23

This excerpt from Hugging Face's NLP course provides a comprehensive overview of tokenization techniques used in natural language processing. Tokenizers are essential tools for transforming raw text into numerical data that machine learning models can understand. The text explores various tokenization methods, including word-based, character-based,…

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PyTorch vs Tensorflow: Who Wins in CNN? 11:49

23d ago11:49

11:49

This research paper examines the efficiency of two popular deep learning libraries, TensorFlow and PyTorch, in developing convolutional neural networks. The authors aim to determine if the choice of library impacts the overall performance of the system during training and design. They evaluate both libraries using six criteria: user-friendliness, a…

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Google's 43 Rules for Machine Learning 9:28

23d ago9:28

9:28

This document provides a comprehensive set of rules for building and deploying machine learning systems, focusing on best practices gleaned from Google’s extensive experience. The document is divided into sections that cover the key stages of the machine learning process, including launching a product without ML, designing and implementing metrics,…

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Do we Need the Mamba Mindset when LLMs Fail? MoE Mamba and SSMs 11:57

24d ago11:57

11:57

The research paper "MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts" explores a novel approach to language modeling by combining State Space Models (SSMs), which offer linear-time inference and strong performance in long-context tasks, with Mixture of Experts (MoE), a technique that scales model parameters while minimizing…

1
Agentic Retrieval Augmented Generation (RAG) systems 7:39

26d ago7:39

7:39

We discuss how to build Agentic Retrieval Augmented Generation (RAG) systems, which use AI agents to retrieve information from various sources to answer user queries. The author details the challenges he faced when building an Agentic RAG system to answer customer support questions, and provides insights into techniques like prompt engineering and …

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Let's Get Activated! Why Non-Linear Activation Matters 7:15

26d ago7:15

7:15

Let's get RE(a)L, U! This research paper explores the impact of different activation functions, specifically ReLU and L-ReLU, on the performance of deep learning models. The authors investigate how the choice of activation function, along with factors like the number of parameters and the shape of the model architecture, influence model accuracy ac…

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Read the Whole Bible Every Year? 23:39

29d ago23:39

23:39

Attempting to read the Bible through each year is a source of frustration and guilt to many as they repeatedly fall behind their reading schedule. Is this yearly ritual a spiritual discipline that advances discipleship or does it substitute a false sense of breadth for real depth?Av Michael Gray

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Reviewing Stanford on Linear Regression and Gradient Descent 8:25

29d ago8:25

8:25

This lecture from Stanford University's CS229 course, "Machine Learning," focuses on the theory and practice of linear regression and gradient descent, two fundamental machine learning algorithms. The lecture begins by motivating linear regression as a simple supervised learning algorithm for regression problems where the goal is to predict a conti…

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Where'd My Gradient Go? It Vanished! 8:39

1M ago8:39

8:39

This video discusses the vanishing gradient problem, a significant challenge in training deep neural networks. The speaker explains how, as a neural network becomes deeper, gradients—measures of how changes in network parameters affect the loss function—can decrease exponentially, leading to a situation where early layers of the network are effecti…

1
Automating Scientific Discovery: ScienceAgentBench 7:38

1M ago7:38

7:38

A scientific paper exploring the development and evaluation of language agents for automating data-driven scientific discovery. The authors introduce a new benchmark called ScienceAgentBench, which consists of 102 diverse tasks extracted from peer-reviewed publications across four disciplines: Bioinformatics, Computational Chemistry, Geographical I…

1
How to Speed up Deep Learning with GPUs 4:13

1M ago4:13

4:13

We discuss how to utilize the processing power of Graphics Processing Units (GPUs) to speed up deep learning calculations, particularly in the context of training neural networks. It outlines how to assign data to different GPUs to minimize data transfer times, a crucial aspect of performance optimization. The text highlights the importance of unde…

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Accidents, Traffic, and Efficiency: AI for Transportation and Logistics 15:15

1M ago15:15

15:15

This paper provides a comprehensive overview of deep generative models (DGMs) and their applications within transportation research. It begins by outlining the fundamental principles and concepts of DGMs, focusing on various model types such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, and Diffusion…

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AI is Watching Me on CCTV! But I Guess I'm Safer... 9:32

1M ago9:32

9:32

This research paper presents the development and evaluation of an AI-driven Smart Video Solution (SVS) designed to enhance community safety. The SVS utilizes existing CCTV infrastructure and leverages recent advancements in AI for anomaly detection, leveraging pose-based data to ensure privacy. The system provides real-time alerts to stakeholders t…

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Mathematics for Machine Learning: An Overview 16:56

1M ago16:56

16:56

The book titled "Mathematics for Machine Learning" explains various mathematical concepts that are essential for understanding machine learning algorithms, including linear algebra, analytic geometry, vector calculus, and probability. It also discusses topics such as model selection, parameter estimation, dimensionality reduction, and classificatio…

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Deep Convolutional Neural Networks (D-CNNs) for Breast Cancer Detection 7:27

1M ago7:27

7:27

Here we discuss three different papers (see links below) on using D-CNNs to detect breast cancer. The first source details the development and evaluation of HIPPO, a novel explainable AI method that enhances the interpretability and trustworthiness of ABMIL models in computational pathology. HIPPO aims to address the challenges of opaque decision-m…

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AI and Methods for Enhancing Human Intelligence, from LessWrong 21:25

1M ago21:25

21:25

This LessWrong post explores various methods to enhance human intelligence, aiming to create individuals with significantly higher cognitive abilities than the current population. The author, TsviBT, proposes numerous approaches ranging from gene editing to brain-computer interfaces and brain emulation, discussing their potential benefits and drawb…

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A Five-Step Roadmap for Machine Learning Engineer Careers 22:15

1M ago22:15

22:15

The first source is a blog post by Max Mynter, a machine learning engineer, outlining a five-to-seven step roadmap for becoming a machine learning engineer. The post emphasizes the importance of both software engineering and data science skills alongside mathematics and domain knowledge. It then offers concrete resources, including courses and book…

1
Generalization in Classification 10:19

1M ago10:19

10:19

We discusses the importance of generalization in classification, where the goal is to train a model that can accurately predict labels for previously unseen data. The text first explores the role of test sets in evaluating model performance, emphasizing the need to use them sparingly and cautiously to avoid overfitting. It then introduces the conce…

1
Scottish AI: Laughter Detection in Machine Learning 5:49

1M ago5:49

5:49

Recognizing laughter in audio is actually a very difficult ML problem, filled with failure. Much like most comedians' jokes. Let's hope some good stuff survives. This is a review of a student's final year project for a University of Edinburgh computer science course. The project focused on creating a machine learning model to detect laughter in vid…

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Applications of GraphML like Predicting Protein Folding 9:48

1M ago9:48

9:48

Solving an impossible mystery... forget what you thought was possible! This is a discussion of a video from Stanford's CS224W course which focuses on the many applications of graph machine learning, a field that utilizes graph data structures to solve complex problems. The speaker highlights different tasks and their associated applications, classi…

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Does RAG Even Scale? EyeLevel vs LangChain 7:55

1M ago7:55

7:55

A research team from EyeLevel.ai has found that vector databases, which are commonly used in RAG (Retrieval-Augmented Generation) systems, have a scaling problem. Their research shows that the accuracy of vector similarity search degrades significantly as the number of pages in the database increases, leading to a substantial performance hit. This …

1
WHY Are Probability And Stats Foundational to ML and DL? 13:39

1M ago13:39

13:39

Probability and statistics are fundamental components of machine learning (ML) and deep learning (DL) because they provide the mathematical framework for understanding and analyzing data, which is crucial for making predictions and decisions. This excerpt from the "Dive into Deep Learning" documentation explains the essential concepts of probabilit…

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Finally? Super-Accurate Weather Forecasts with AI 8:04

1M ago8:04

8:04

This research paper examines a new deep-learning approach to optimizing weather forecasts by adjusting initial conditions. The authors test their method on the 2021 Pacific Northwest heatwave, finding that small changes to initial conditions can significantly improve the accuracy of 10-day forecasts using both the GraphCast and Pangu-Weather deep-l…

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Let's Descend: Why Calculus is Important in Deep Learning 11:00

1M ago11:00

11:00

An introduction to the fundamental concepts of calculus, explaining how they are essential for understanding deep learning. It begins by illustrating the concept of a limit using the calculation of a circle's area, before introducing the concept of a derivative, which describes a function's rate of change. It then extends these concepts to multivar…

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OMG OpenAI's o1 is a LogicQuake! The AI Reasoning Layer, Sequoia Capital 6:42

1M ago6:42

6:42

The source, "Generative AI's Act o1: The Reasoning Era Begins | Sequoia Capital," discusses the evolution of AI models from simply mimicking patterns to engaging in more deliberate reasoning. The authors argue that the next frontier in AI is the development of "System 2" thinking, where models can reason through complex problems and make decisions …

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Assemble Your Team! OpenAI's Swarm 8:43

1M ago8:43

8:43

Swarm is an experimental, educational framework from OpenAI that explores ergonomic interfaces for multi-agent systems. It is not intended for production use, but serves as a learning tool for developers interested in multi-agent orchestration. Swarm uses two main concepts: Agents and handoffs. Agents are entities that encapsulate instructions and …

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Bio-Origami: The 2024 Nobel Prize in Chemistry for AI Protein Folding (DeepMind) 10:49

1M ago10:49

10:49

The provided sources detail the groundbreaking work of three scientists who were awarded the 2024 Nobel Prize in Chemistry for their contributions to protein structure prediction using artificial intelligence. David Baker, a biochemist, developed a computer program to create entirely new proteins, while Demis Hassabis and John Jumper, from Google D…

1
Anthropic Controversy: Dario Amodei's Machines of Loving Grace Post 13:40

1M ago13:40

13:40

Dario Amodei, CEO of Anthropic, argues that powerful AI could revolutionize various fields, including healthcare, neuroscience, economics, and governance, within 5-10 years. He envisions a future where AI could cure most diseases, eradicate poverty, and even promote democracy. However, this optimistic vision is met with skepticism from Reddit users…

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RAG for LLMs: An Overview 19:34

1M ago19:34

19:34

This paper examines the rapidly developing field of Retrieval-Augmented Generation (RAG), which aims to improve the capabilities of Large Language Models (LLMs) by incorporating external knowledge. The paper reviews the evolution of RAG paradigms, from the early "Naive RAG" to the more sophisticated "Advanced RAG" and "Modular RAG" approaches. It e…

1
Poles Apart in the Church? 21:47

1M ago21:47

21:47

The church is not immune to polarity that all too often leads to contention and division—the opposite of biblical unity. This study in Ephesians aims to transform your understanding of the nature of biblical unity and its priority in the life of each Christian.Av Michael Gray

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Certainty and OOD Detection in Medical Images and Multiple Sclerosis 7:14

1M ago7:14

7:14

This research paper investigates the challenges of detecting Out-of-Distribution (OOD) inputs in medical image segmentation tasks, particularly in the context of Multiple Sclerosis (MS) lesion segmentation. The authors propose a novel evaluation framework that uses 14 different sources of OOD, including synthetic artifacts and real-world variations…

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LLM DIFF Transformer with SoftMax Subtraction 12:48

1M ago12:48

12:48

This paper presents a new architecture for large language models called DIFF Transformer. The paper argues that conventional Transformers over-allocate attention to irrelevant parts of the input, drowning out the signal needed for accurate output. DIFF Transformer tackles this issue by using a differential attention mechanism that subtracts two sof…

1
Data Pruning to Improve AI Performance 17:00

1M ago17:00

17:00

The source is a blog post that describes the author's journey in exploring the potential of data pruning to improve the performance of AI models. They start by discussing the Minipile method, a technique for creating high-quality datasets by clustering and manually discarding low-quality content. The author then explores the concept of "foundationa…

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OpenAI's o1 and Journey Learning 7:28

1M ago7:28

7:28

This paper details the authors' research journey to replicate OpenAI's "O1" language model, which is designed to solve complex reasoning tasks. The researchers document their process with detailed insights, hypotheses, and challenges encountered. They present a novel paradigm called "Journey Learning" that enables models to learn the complete explo…

1
Forward and Backpropagation 6:25

1M ago6:25

6:25

Let's get into the core processes of forward propagation and backpropagation in neural networks, which form the foundation of training these models. Forward propagation involves calculating the outputs of a neural network, starting with the input layer and moving towards the output layer. Backpropagation then calculates the gradients of the network…

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MLE-bench for Engineering Tasks 10:23

1M ago10:23

10:23

This research introduces MLE-bench, a benchmark for evaluating how well AI agents perform machine learning engineering tasks. The benchmark is comprised of 75 Kaggle competitions, chosen for their difficulty and representativeness of real-world ML engineering skills. Researchers evaluated several state-of-the-art language models on MLE-bench, findi…

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AI in Dentistry: CNNs for Segmenting and Classifying Dental Images 8:33

1M ago8:33

8:33

This systematic literature review investigates the use of convolutional neural networks (CNNs) for segmenting and classifying dental images. The review analyzes 45 studies that employed CNNs for various tasks, including tooth detection, periapical lesion detection, caries identification, and age and sex determination. The authors explore the differ…

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AI in Dentistry: Diagnosing TMD with MRIs 7:14

1M ago7:14

7:14

This research paper proposes an AI-driven diagnostic system for Temporomandibular Joint Disorders (TMD) using MRI images. The system employs a segmentation method to identify key anatomical structures like the temporal bone, temporomandibular joint (TMJ) disc, and condyle. Using these identified structures, the system utilizes a decision tree based…

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AI in Dentistry: ChatGPT for Dental Diagnosis and Treatment 9:25

1M ago9:25

9:25

This research explores the potential for integrating ChatGPT and large language models (LLMs) into dental diagnostics and treatment. The authors investigate the use of these AI tools in various areas of dentistry, including diagnosis, treatment planning, patient education, and dental research. The study examines the benefits and limitations of LLMs…

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AI in Dentistry: OMG, Machine Learning that links TMD and OSA 12:28

1M ago12:28

12:28

This research paper explores the link between temporomandibular disorder (TMD) and obstructive sleep apnea (OSA). The authors created a machine learning algorithm to predict the presence of OSA in TMD patients using multimodal data, including clinical characteristics, portable polysomnography, X-ray, and MRI. Their model achieved high accuracy, wit…

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AI in Dentistry: Reading Intraoral Radiographs 5:13

1M ago5:13

5:13

This article describes a clinical validation study that investigates the effectiveness of a deep learning algorithm for detecting dental anomalies in intraoral radiographs. The algorithm is trained to detect six common anomaly types and is compared to the performance of dentists who evaluate the images without algorithmic assistance. The study util…

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AI in Dentistry: 3D X-Rays? Cone Beam Computed Tomography 8:16

1M ago8:16

8:16

Read more: https://arxiv.org/pdf/2306.03025Av Brian Carter

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