際際滷shows by User: RiteshKanjee / http://www.slideshare.net/images/logo.gif 際際滷shows by User: RiteshKanjee / Tue, 09 Apr 2024 13:29:31 GMT 際際滷Share feed for 際際滷shows by User: RiteshKanjee Top 7 AI Software Engineers - Similar to Devin /slideshow/top-7-ai-software-engineers-similar-to-devin/267180429 top7aisoftwareengineers-240409132931-29f86924
Top 7 AI Software Engineers - Similar to Devin 1. OpenDevin 2. Devika 3. MetaGPT 4. Auto-GPT 5. SWE-Agent 6. Replit Code Repair 7. MetaGPT Learn more at Augmented AI https://bit.ly/augmentedaiuniversity #devin #ai #llm #chatgpt #softwareengineer ]]>
Top 7 AI Software Engineers - Similar to Devin 1. OpenDevin 2. Devika 3. MetaGPT 4. Auto-GPT 5. SWE-Agent 6. Replit Code Repair 7. MetaGPT Learn more at Augmented AI https://bit.ly/augmentedaiuniversity #devin #ai #llm #chatgpt #softwareengineer ]]> Tue, 09 Apr 2024 13:29:31 GMT /slideshow/top-7-ai-software-engineers-similar-to-devin/267180429 RiteshKanjee@slideshare.net(RiteshKanjee) Top 7 AI Software Engineers - Similar to Devin RiteshKanjee Top 7 AI Software Engineers - Similar to Devin 1. OpenDevin 2. Devika 3. MetaGPT 4. Auto-GPT 5. SWE-Agent 6. Replit Code Repair 7. MetaGPT Learn more at Augmented AI https://bit.ly/augmentedaiuniversity #devin #ai #llm #chatgpt #softwareengineer <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/top7aisoftwareengineers-240409132931-29f86924-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Top 7 AI Software Engineers - Similar to Devin 1. OpenDevin 2. Devika 3. MetaGPT 4. Auto-GPT 5. SWE-Agent 6. Replit Code Repair 7. MetaGPT Learn more at Augmented AI https://bit.ly/augmentedaiuniversity #devin #ai #llm #chatgpt #softwareengineer
Top 7 AI Software Engineers - Similar to Devin from Ritesh Kanjee
]]> 65 0 https://cdn.slidesharecdn.com/ss_thumbnails/top7aisoftwareengineers-240409132931-29f86924-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 XAI Grok-1 is now open-source, claiming the title of the largest open-source LLM ever built! /slideshow/xai-grok1-is-now-opensource-claiming-the-title-of-the-largest-opensource-llm-ever-built/266861130 grok0-240319071906-50141e87
Grok-1 With a whopping 314-billion parameters, Grok-1 leverages a Mixture of Experts (MoE) model, actively using 86 billion parameters to supercharge its processing power. Quick Look at Grok-1 Specs: - М Parameters: 314 billion, with 25% active per token. - 鏝 Architecture: 8 Expert MoE, 2 experts per token. - Layers: 64 transformer layers with cutting-edge attention and dense blocks. - Tokenization: SentencePiece with a vast 131,072 vocab size. - Embedding & Positioning: 6,144-size embeddings with matching rotary positional embeddings. - Attention: 48 heads for queries, 8 for keys/values, each head sized at 128. - Context Length: Handles an impressive 8,192 tokens, utilizing bf16 precision.]]>
Grok-1 With a whopping 314-billion parameters, Grok-1 leverages a Mixture of Experts (MoE) model, actively using 86 billion parameters to supercharge its processing power. Quick Look at Grok-1 Specs: - М Parameters: 314 billion, with 25% active per token. - 鏝 Architecture: 8 Expert MoE, 2 experts per token. - Layers: 64 transformer layers with cutting-edge attention and dense blocks. - Tokenization: SentencePiece with a vast 131,072 vocab size. - Embedding & Positioning: 6,144-size embeddings with matching rotary positional embeddings. - Attention: 48 heads for queries, 8 for keys/values, each head sized at 128. - Context Length: Handles an impressive 8,192 tokens, utilizing bf16 precision.]]> Tue, 19 Mar 2024 07:19:06 GMT /slideshow/xai-grok1-is-now-opensource-claiming-the-title-of-the-largest-opensource-llm-ever-built/266861130 RiteshKanjee@slideshare.net(RiteshKanjee) XAI Grok-1 is now open-source, claiming the title of the largest open-source LLM ever built! RiteshKanjee Grok-1 With a whopping 314-billion parameters, Grok-1 leverages a Mixture of Experts (MoE) model, actively using 86 billion parameters to supercharge its processing power. Quick Look at Grok-1 Specs: - М Parameters: 314 billion, with 25% active per token. - 鏝 Architecture: 8 Expert MoE, 2 experts per token. - Layers: 64 transformer layers with cutting-edge attention and dense blocks. - Tokenization: SentencePiece with a vast 131,072 vocab size. - Embedding & Positioning: 6,144-size embeddings with matching rotary positional embeddings. - Attention: 48 heads for queries, 8 for keys/values, each head sized at 128. - Context Length: Handles an impressive 8,192 tokens, utilizing bf16 precision. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/grok0-240319071906-50141e87-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Grok-1 With a whopping 314-billion parameters, Grok-1 leverages a Mixture of Experts (MoE) model, actively using 86 billion parameters to supercharge its processing power. Quick Look at Grok-1 Specs: - М Parameters: 314 billion, with 25% active per token. - 鏝 Architecture: 8 Expert MoE, 2 experts per token. - Layers: 64 transformer layers with cutting-edge attention and dense blocks. - Tokenization: SentencePiece with a vast 131,072 vocab size. - Embedding &amp; Positioning: 6,144-size embeddings with matching rotary positional embeddings. - Attention: 48 heads for queries, 8 for keys/values, each head sized at 128. - Context Length: Handles an impressive 8,192 tokens, utilizing bf16 precision.
XAI Grok-1 is now open-source, claiming the title of the largest open-source LLM ever built! from Ritesh Kanjee
]]> 32 0 https://cdn.slidesharecdn.com/ss_thumbnails/grok0-240319071906-50141e87-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 8 Steps to Build a LangChain RAG Chatbot. /slideshow/8-steps-to-build-a-langchain-rag-chatbot/266744548 langchaindeck0-240312075948-fbfd0de1
Retrieval Augmented Generation (RAG) is a methodology that enhances large language models (LLMs) by integrating external knowledge sources into their processes. Langchain implements RAG by adding a retrieval step to the prompt and LLM, forming a "retrieval-augmented generation" chain. This augmentation improves the accuracy of generated output by providing relevant context from external sources. RAG facilitates more informed and contextually accurate responses from LLMs, contributing to better performance in various tasks such as question answering and content generation. Source - @akshay_pachaar]]>
Retrieval Augmented Generation (RAG) is a methodology that enhances large language models (LLMs) by integrating external knowledge sources into their processes. Langchain implements RAG by adding a retrieval step to the prompt and LLM, forming a "retrieval-augmented generation" chain. This augmentation improves the accuracy of generated output by providing relevant context from external sources. RAG facilitates more informed and contextually accurate responses from LLMs, contributing to better performance in various tasks such as question answering and content generation. Source - @akshay_pachaar]]> Tue, 12 Mar 2024 07:59:48 GMT /slideshow/8-steps-to-build-a-langchain-rag-chatbot/266744548 RiteshKanjee@slideshare.net(RiteshKanjee) 8 Steps to Build a LangChain RAG Chatbot. RiteshKanjee Retrieval Augmented Generation (RAG) is a methodology that enhances large language models (LLMs) by integrating external knowledge sources into their processes. Langchain implements RAG by adding a retrieval step to the prompt and LLM, forming a "retrieval-augmented generation" chain. This augmentation improves the accuracy of generated output by providing relevant context from external sources. RAG facilitates more informed and contextually accurate responses from LLMs, contributing to better performance in various tasks such as question answering and content generation. Source - @akshay_pachaar <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/langchaindeck0-240312075948-fbfd0de1-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Retrieval Augmented Generation (RAG) is a methodology that enhances large language models (LLMs) by integrating external knowledge sources into their processes. Langchain implements RAG by adding a retrieval step to the prompt and LLM, forming a &quot;retrieval-augmented generation&quot; chain. This augmentation improves the accuracy of generated output by providing relevant context from external sources. RAG facilitates more informed and contextually accurate responses from LLMs, contributing to better performance in various tasks such as question answering and content generation. Source - @akshay_pachaar
8 Steps to Build a LangChain RAG Chatbot. from Ritesh Kanjee
]]> 889 0 https://cdn.slidesharecdn.com/ss_thumbnails/langchaindeck0-240312075948-fbfd0de1-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 What is Computer Vision? /slideshow/what-is-computer-vision-252133151/252133151 p1-220706105308-12cf2427
If you are new to Computer Vision and just starting out, here is a simple and fun comic to showcase an application of it. There is a huge problem of couriered packages getting stolen from the front porch of people's homes. Let's see how they tackle this problem using AI and Computer Vision.]]>
If you are new to Computer Vision and just starting out, here is a simple and fun comic to showcase an application of it. There is a huge problem of couriered packages getting stolen from the front porch of people's homes. Let's see how they tackle this problem using AI and Computer Vision.]]> Wed, 06 Jul 2022 10:53:08 GMT /slideshow/what-is-computer-vision-252133151/252133151 RiteshKanjee@slideshare.net(RiteshKanjee) What is Computer Vision? RiteshKanjee If you are new to Computer Vision and just starting out, here is a simple and fun comic to showcase an application of it. There is a huge problem of couriered packages getting stolen from the front porch of people's homes. Let's see how they tackle this problem using AI and Computer Vision. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/p1-220706105308-12cf2427-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> If you are new to Computer Vision and just starting out, here is a simple and fun comic to showcase an application of it. There is a huge problem of couriered packages getting stolen from the front porch of people&#39;s homes. Let&#39;s see how they tackle this problem using AI and Computer Vision.
What is Computer Vision? from Ritesh Kanjee
]]> 11 0 https://cdn.slidesharecdn.com/ss_thumbnails/p1-220706105308-12cf2427-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Fun and Easy UART - How the UART Protocol Works /slideshow/fun-and-easy-uart-how-the-uart-protocol-works/67638576 uart-161025171059
Learn how the UART Protocol works. A universal asynchronous receiver/transmitter is a computer hardware device for asynchronous serial communication in which the data format and transmission speeds are configurable. The electric signaling levels and methods (such as differential signaling, etc.) are handled by a driver circuit external to the UART.]]>
Learn how the UART Protocol works. A universal asynchronous receiver/transmitter is a computer hardware device for asynchronous serial communication in which the data format and transmission speeds are configurable. The electric signaling levels and methods (such as differential signaling, etc.) are handled by a driver circuit external to the UART.]]> Tue, 25 Oct 2016 17:10:59 GMT /slideshow/fun-and-easy-uart-how-the-uart-protocol-works/67638576 RiteshKanjee@slideshare.net(RiteshKanjee) Fun and Easy UART - How the UART Protocol Works RiteshKanjee Learn how the UART Protocol works. A universal asynchronous receiver/transmitter is a computer hardware device for asynchronous serial communication in which the data format and transmission speeds are configurable. The electric signaling levels and methods (such as differential signaling, etc.) are handled by a driver circuit external to the UART. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/uart-161025171059-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Learn how the UART Protocol works. A universal asynchronous receiver/transmitter is a computer hardware device for asynchronous serial communication in which the data format and transmission speeds are configurable. The electric signaling levels and methods (such as differential signaling, etc.) are handled by a driver circuit external to the UART.
Fun and Easy UART - How the UART Protocol Works from Ritesh Kanjee
]]> 878 2 https://cdn.slidesharecdn.com/ss_thumbnails/uart-161025171059-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Fun and Easy Kalman filter Tutorial - Using Pokemon Example /slideshow/fun-and-easy-kalman-filter-tutorial-using-pokemon-example-65524031/65524031 kalmanfilter-160830195414
Why You Should Use The Kalman Filter Tutorial- #Pokemon Example Kalman filtering, also known as linear quadratic estimation, is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. K叩lm叩n, one of the primary developers of its theory. This tutorial breaks down the components of the Kalman filter making easy for anyone to understand. It introduces you to the concepts of the Kalman filter using the pokemon analogy. To learn more on PCB Design, Kalman Filter Tutorial and FPGA's then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :)]]>
Why You Should Use The Kalman Filter Tutorial- #Pokemon Example Kalman filtering, also known as linear quadratic estimation, is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. K叩lm叩n, one of the primary developers of its theory. This tutorial breaks down the components of the Kalman filter making easy for anyone to understand. It introduces you to the concepts of the Kalman filter using the pokemon analogy. To learn more on PCB Design, Kalman Filter Tutorial and FPGA's then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :)]]> Tue, 30 Aug 2016 19:54:14 GMT /slideshow/fun-and-easy-kalman-filter-tutorial-using-pokemon-example-65524031/65524031 RiteshKanjee@slideshare.net(RiteshKanjee) Fun and Easy Kalman filter Tutorial - Using Pokemon Example RiteshKanjee Why You Should Use The Kalman Filter Tutorial- #Pokemon Example Kalman filtering, also known as linear quadratic estimation, is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. K叩lm叩n, one of the primary developers of its theory. This tutorial breaks down the components of the Kalman filter making easy for anyone to understand. It introduces you to the concepts of the Kalman filter using the pokemon analogy. To learn more on PCB Design, Kalman Filter Tutorial and FPGA's then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/kalmanfilter-160830195414-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Why You Should Use The Kalman Filter Tutorial- #Pokemon Example Kalman filtering, also known as linear quadratic estimation, is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. K叩lm叩n, one of the primary developers of its theory. This tutorial breaks down the components of the Kalman filter making easy for anyone to understand. It introduces you to the concepts of the Kalman filter using the pokemon analogy. To learn more on PCB Design, Kalman Filter Tutorial and FPGA&#39;s then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :)
Fun and Easy Kalman filter Tutorial - Using Pokemon Example from Ritesh Kanjee
]]> 1140 5 https://cdn.slidesharecdn.com/ss_thumbnails/kalmanfilter-160830195414-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Feature detection - Image Processing /slideshow/feature-detection-image-processing/55769795 featuredetectionslideshare-151203072138-lva1-app6892
In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions. This lecture teaches you the basics of feature detection. https://www.udemy.com/learn-computer-vision-machine-vision-and-image-processing-in-labview/?couponCode=際際滷Share ]]>
In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions. This lecture teaches you the basics of feature detection. https://www.udemy.com/learn-computer-vision-machine-vision-and-image-processing-in-labview/?couponCode=際際滷Share ]]> Thu, 03 Dec 2015 07:21:38 GMT /slideshow/feature-detection-image-processing/55769795 RiteshKanjee@slideshare.net(RiteshKanjee) Feature detection - Image Processing RiteshKanjee In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions. This lecture teaches you the basics of feature detection. https://www.udemy.com/learn-computer-vision-machine-vision-and-image-processing-in-labview/?couponCode=際際滷Share <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/featuredetectionslideshare-151203072138-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions. This lecture teaches you the basics of feature detection. https://www.udemy.com/learn-computer-vision-machine-vision-and-image-processing-in-labview/?couponCode=際際滷Share
Feature detection - Image Processing from Ritesh Kanjee
]]> 2353 9 https://cdn.slidesharecdn.com/ss_thumbnails/featuredetectionslideshare-151203072138-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 What is machine vision slide share /slideshow/what-is-machine-vision-slide-share/55769560 whatismachinevisionslideshare-151203071031-lva1-app6891
This lecture discusses the difference between computer and machine vision. It introduces you to the world of image processing. If you would like to learn how to use cameras to detect objects within an image as well as track them, then check out this lecture for more details. If you find openCV or matlab intimidating then check out this course we take you step by step through creating your own vision based apps. https://www.udemy.com/learn-computer-vision-machine-vision-and-image-processing-in-labview/?couponCode=際際滷Share]]>
This lecture discusses the difference between computer and machine vision. It introduces you to the world of image processing. If you would like to learn how to use cameras to detect objects within an image as well as track them, then check out this lecture for more details. If you find openCV or matlab intimidating then check out this course we take you step by step through creating your own vision based apps. https://www.udemy.com/learn-computer-vision-machine-vision-and-image-processing-in-labview/?couponCode=際際滷Share]]> Thu, 03 Dec 2015 07:10:31 GMT /slideshow/what-is-machine-vision-slide-share/55769560 RiteshKanjee@slideshare.net(RiteshKanjee) What is machine vision slide share RiteshKanjee This lecture discusses the difference between computer and machine vision. It introduces you to the world of image processing. If you would like to learn how to use cameras to detect objects within an image as well as track them, then check out this lecture for more details. If you find openCV or matlab intimidating then check out this course we take you step by step through creating your own vision based apps. https://www.udemy.com/learn-computer-vision-machine-vision-and-image-processing-in-labview/?couponCode=際際滷Share <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/whatismachinevisionslideshare-151203071031-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This lecture discusses the difference between computer and machine vision. It introduces you to the world of image processing. If you would like to learn how to use cameras to detect objects within an image as well as track them, then check out this lecture for more details. If you find openCV or matlab intimidating then check out this course we take you step by step through creating your own vision based apps. https://www.udemy.com/learn-computer-vision-machine-vision-and-image-processing-in-labview/?couponCode=際際滷Share
What is machine vision slide share from Ritesh Kanjee
]]> 2524 10 https://cdn.slidesharecdn.com/ss_thumbnails/whatismachinevisionslideshare-151203071031-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-RiteshKanjee-48x48.jpg?cb=1712747739 Making AI and Computer Vision Easy www.augmentedstartups.com https://cdn.slidesharecdn.com/ss_thumbnails/top7aisoftwareengineers-240409132931-29f86924-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/top-7-ai-software-engineers-similar-to-devin/267180429 Top 7 AI Software Engi... https://cdn.slidesharecdn.com/ss_thumbnails/grok0-240319071906-50141e87-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/xai-grok1-is-now-opensource-claiming-the-title-of-the-largest-opensource-llm-ever-built/266861130 XAI Grok-1 is now open... https://cdn.slidesharecdn.com/ss_thumbnails/langchaindeck0-240312075948-fbfd0de1-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/8-steps-to-build-a-langchain-rag-chatbot/266744548 8 Steps to Build a Lan...