�ݺ�ߣshows by User: LoicMerckel

�ݺ�ߣshows by User: LoicMerckel / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: LoicMerckel / Tue, 07 Nov 2023 21:06:35 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: LoicMerckel Introduction to LLMs /slideshow/introduction-to-llms/263167254 introllmslhnov2023shared-231107210635-cba19bd0
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable. (This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)]]>
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable. (This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)]]> Tue, 07 Nov 2023 21:06:35 GMT /slideshow/introduction-to-llms/263167254 LoicMerckel@slideshare.net(LoicMerckel) Introduction to LLMs LoicMerckel A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable. (This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introllmslhnov2023shared-231107210635-cba19bd0-thumbnail.jpg?width=120&height=120&fit=bounds" /> A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable. (This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)

Introduction to LLMs from Loic Merckel

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0 Intro to LLMs /slideshow/intro-to-llms/261792403 introllmssep2023shared-231005135045-0a04b5c2
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable. (Note: Discover a slightly updated version of this deck at slideshare.net/LoicMerckel/introduction-to-llms.)]]>
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable. (Note: Discover a slightly updated version of this deck at slideshare.net/LoicMerckel/introduction-to-llms.)]]> Thu, 05 Oct 2023 13:50:44 GMT /slideshow/intro-to-llms/261792403 LoicMerckel@slideshare.net(LoicMerckel) Intro to LLMs LoicMerckel A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable. (Note: Discover a slightly updated version of this deck at slideshare.net/LoicMerckel/introduction-to-llms.) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introllmssep2023shared-231005135045-0a04b5c2-thumbnail.jpg?width=120&height=120&fit=bounds" /> A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable. (Note: Discover a slightly updated version of this deck at slideshare.net/LoicMerckel/introduction-to-llms.)

Intro to LLMs from Loic Merckel

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0 Generative Models and ChatGPT /slideshow/generative-models-and-chatgpt/257165422 loic-merckel-generative-models-and-chatgpt-mar2023-shared-230404215357-3e48b4a1
A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.]]>
A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.]]> Tue, 04 Apr 2023 21:53:57 GMT /slideshow/generative-models-and-chatgpt/257165422 LoicMerckel@slideshare.net(LoicMerckel) Generative Models and ChatGPT LoicMerckel A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/loic-merckel-generative-models-and-chatgpt-mar2023-shared-230404215357-3e48b4a1-thumbnail.jpg?width=120&height=120&fit=bounds" /> A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.

Generative Models and ChatGPT from Loic Merckel

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0 iQHorse—Towards Beating the Market /slideshow/iqhorsetowards-beating-the-market/231239862 loic-merckel-iqhorse-apr2020-shared-200401193054
Brief introduction to iQHorse (iqhorse.com), an online magazine focusing on Hong Kong's renowned horse racing activities and using advanced machine learning techniques to estimate winning probabilities.]]>
Brief introduction to iQHorse (iqhorse.com), an online magazine focusing on Hong Kong's renowned horse racing activities and using advanced machine learning techniques to estimate winning probabilities.]]> Wed, 01 Apr 2020 19:30:54 GMT /slideshow/iqhorsetowards-beating-the-market/231239862 LoicMerckel@slideshare.net(LoicMerckel) iQHorse—Towards Beating the Market LoicMerckel Brief introduction to iQHorse (iqhorse.com), an online magazine focusing on Hong Kong's renowned horse racing activities and using advanced machine learning techniques to estimate winning probabilities. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/loic-merckel-iqhorse-apr2020-shared-200401193054-thumbnail.jpg?width=120&height=120&fit=bounds" /> Brief introduction to iQHorse (iqhorse.com), an online magazine focusing on Hong Kong's renowned horse racing activities and using advanced machine learning techniques to estimate winning probabilities.

iQHorse—Towards Beating the Market from Loic Merckel

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0 Ideation: First Step Towards Innovation /slideshow/ideation-first-step-towards-innovation/230282254 loic-merckel-lh-brownbag-feb2020-shared-200315124357
A definition of innovation is presented with a following discussion on why it matters. Then, the focus is set on the first stage of innovation, that is ideation. One can distinguish three pillars to enable a creative culture: people, organization and processes. Each of them is succinctly introduced. The last part of the talk contained some remarks that might be perceived as a bit tendentious. For example, the currently very fashionable "design thinking workshops" are questioned—not the concept of design thinking itself, but how too often it is implemented through sporadic off-site workshops without a well-honed problem statement and without subsequent follow-up. Note: this talk was delivered during a "brownbag" lunch, where the lunch consisted of pre-cut pizza—hence slide #2.]]>
A definition of innovation is presented with a following discussion on why it matters. Then, the focus is set on the first stage of innovation, that is ideation. One can distinguish three pillars to enable a creative culture: people, organization and processes. Each of them is succinctly introduced. The last part of the talk contained some remarks that might be perceived as a bit tendentious. For example, the currently very fashionable "design thinking workshops" are questioned—not the concept of design thinking itself, but how too often it is implemented through sporadic off-site workshops without a well-honed problem statement and without subsequent follow-up. Note: this talk was delivered during a "brownbag" lunch, where the lunch consisted of pre-cut pizza—hence slide #2.]]> Sun, 15 Mar 2020 12:43:57 GMT /slideshow/ideation-first-step-towards-innovation/230282254 LoicMerckel@slideshare.net(LoicMerckel) Ideation: First Step Towards Innovation LoicMerckel A definition of innovation is presented with a following discussion on why it matters. Then, the focus is set on the first stage of innovation, that is ideation. One can distinguish three pillars to enable a creative culture: people, organization and processes. Each of them is succinctly introduced. The last part of the talk contained some remarks that might be perceived as a bit tendentious. For example, the currently very fashionable "design thinking workshops" are questioned—not the concept of design thinking itself, but how too often it is implemented through sporadic off-site workshops without a well-honed problem statement and without subsequent follow-up. Note: this talk was delivered during a "brownbag" lunch, where the lunch consisted of pre-cut pizza—hence slide #2. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/loic-merckel-lh-brownbag-feb2020-shared-200315124357-thumbnail.jpg?width=120&height=120&fit=bounds" /> A definition of innovation is presented with a following discussion on why it matters. Then, the focus is set on the first stage of innovation, that is ideation. One can distinguish three pillars to enable a creative culture: people, organization and processes. Each of them is succinctly introduced. The last part of the talk contained some remarks that might be perceived as a bit tendentious. For example, the currently very fashionable "design thinking workshops" are questioned—not the concept of design thinking itself, but how too often it is implemented through sporadic off-site workshops without a well-honed problem statement and without subsequent follow-up. Note: this talk was delivered during a "brownbag" lunch, where the lunch consisted of pre-cut pizza—hence slide #2.

Ideation: First Step Towards Innovation from Loic Merckel

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0 Are Decisions From a Single Point Wise? /slideshow/are-decisions-from-a-single-point-wise-230148580/230148580 loic-merckel-zerog-mar2018-shared-200312132100
After observing that many projects fail in spite of a promising spreadsheet-based forecast, we highlight one of the fundamental problems in planning under uncertainty today. Namely, a single statistic—typically the mean—often fails to properly describe an uncertain number. Furthermore, forecasting a single statistic is very hard; and even in the event where it is accurately forecasted, the underlying fundamentals, i.e., the real world, might decide on a vastly diverging outcome. There exist ways to mitigate the luck factor. Unfortunately, those solutions are often ignored by the vast majority of corporate people (for various reasons that are not discussed). In many cases, those solutions consist in predicting a statistical distribution rather than a single point. We briefly present some of those solutions. In particular, it is probably worth recalling that many machine learning techniques today—extensively relied on in various industries to support business decisions—are actually yielding a single point estimate. We briefly introduce the so-called Bayesian Neural Networks, which aim at predicting distributions. ]]>
After observing that many projects fail in spite of a promising spreadsheet-based forecast, we highlight one of the fundamental problems in planning under uncertainty today. Namely, a single statistic—typically the mean—often fails to properly describe an uncertain number. Furthermore, forecasting a single statistic is very hard; and even in the event where it is accurately forecasted, the underlying fundamentals, i.e., the real world, might decide on a vastly diverging outcome. There exist ways to mitigate the luck factor. Unfortunately, those solutions are often ignored by the vast majority of corporate people (for various reasons that are not discussed). In many cases, those solutions consist in predicting a statistical distribution rather than a single point. We briefly present some of those solutions. In particular, it is probably worth recalling that many machine learning techniques today—extensively relied on in various industries to support business decisions—are actually yielding a single point estimate. We briefly introduce the so-called Bayesian Neural Networks, which aim at predicting distributions. ]]> Thu, 12 Mar 2020 13:21:00 GMT /slideshow/are-decisions-from-a-single-point-wise-230148580/230148580 LoicMerckel@slideshare.net(LoicMerckel) Are Decisions From a Single Point Wise? LoicMerckel After observing that many projects fail in spite of a promising spreadsheet-based forecast, we highlight one of the fundamental problems in planning under uncertainty today. Namely, a single statistic—typically the mean—often fails to properly describe an uncertain number. Furthermore, forecasting a single statistic is very hard; and even in the event where it is accurately forecasted, the underlying fundamentals, i.e., the real world, might decide on a vastly diverging outcome. There exist ways to mitigate the luck factor. Unfortunately, those solutions are often ignored by the vast majority of corporate people (for various reasons that are not discussed). In many cases, those solutions consist in predicting a statistical distribution rather than a single point. We briefly present some of those solutions. In particular, it is probably worth recalling that many machine learning techniques today—extensively relied on in various industries to support business decisions—are actually yielding a single point estimate. We briefly introduce the so-called Bayesian Neural Networks, which aim at predicting distributions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/loic-merckel-zerog-mar2018-shared-200312132100-thumbnail.jpg?width=120&height=120&fit=bounds" /> After observing that many projects fail in spite of a promising spreadsheet-based forecast, we highlight one of the fundamental problems in planning under uncertainty today. Namely, a single statistic—typically the mean—often fails to properly describe an uncertain number. Furthermore, forecasting a single statistic is very hard; and even in the event where it is accurately forecasted, the underlying fundamentals, i.e., the real world, might decide on a vastly diverging outcome. There exist ways to mitigate the luck factor. Unfortunately, those solutions are often ignored by the vast majority of corporate people (for various reasons that are not discussed). In many cases, those solutions consist in predicting a statistical distribution rather than a single point. We briefly present some of those solutions. In particular, it is probably worth recalling that many machine learning techniques today—extensively relied on in various industries to support business decisions—are actually yielding a single point estimate. We briefly introduce the so-called Bayesian Neural Networks, which aim at predicting distributions.

Are Decisions From a Single Point Wise? from Loic Merckel

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/introllmslhnov2023shared-231107210635-cba19bd0-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/introduction-to-llms/263167254 Introduction to LLMs https://cdn.slidesharecdn.com/ss_thumbnails/introllmssep2023shared-231005135045-0a04b5c2-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/intro-to-llms/261792403 Intro to LLMs https://cdn.slidesharecdn.com/ss_thumbnails/loic-merckel-generative-models-and-chatgpt-mar2023-shared-230404215357-3e48b4a1-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/generative-models-and-chatgpt/257165422 Generative Models and ...