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Skill Foundry – ARTIFICIAL INTELLIGENCE WITH PYTHON
INTRODUCTION 3
Setting Up AI Development Environment with Python 7
Understanding Machine Learning — The Heart of AI 11
Supervised Learning Deep Dive — Regression and Classification Models 16
Unsupervised Learning Deep Dive — Discovering Hidden Patterns 21
Neural Networks Fundamentals — Building Brains for AI 26
Project — Build a Neural Network to Classify Handwritten Digits 30
Deep Learning for Image Classification — CNNs Explained 33
Advanced Image Classification — Transfer Learning 37
Natural Language Processing (NLP) Basics with Python 41
Spam Detection Using Machine Learning 45
Deep Learning for Text Classification (with NLP) 48
Computer Vision Basics and Image Classification 51
AI for Automation: Files, Web, and Emails 56
AI Chatbots and Virtual Assistants 61 -
Eyeline Labs VChain – Chain-of-Visual-Thought for Reasoning in Video Generation for better AI physics
https://eyeline-labs.github.io/VChain/
https://github.com/Eyeline-Labs/VChain
Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transitions over time remains a core challenge. In contrast, large language and multimodal models (e.g., GPT-4o) exhibit strong visual state reasoning and future prediction capabilities. To bridge these strengths, we introduce VChain, a novel inference-time chain-of-visual-thought framework that injects visual reasoning signals from multimodal models into video generation. Specifically, VChain contains a dedicated pipeline that leverages large multimodal models to generate a sparse set of critical keyframes as snapshots, which are then used to guide the sparse inference-time tuning of a pre-trained video generator only at these key moments. Our approach is tuning-efficient, introduces minimal overhead and avoids dense supervision. Extensive experiments on complex, multi-step scenarios show that VChain significantly enhances the quality of generated videos.
FEATURED POSTS
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Photography basics: Exposure Value vs Photographic Exposure vs Il/Luminance vs Pixel luminance measurements
Also see: https://www.pixelsham.com/2015/05/16/how-aperture-shutter-speed-and-iso-affect-your-photos/
In photography, exposure value (EV) is a number that represents a combination of a camera’s shutter speed and f-number, such that all combinations that yield the same exposure have the same EV (for any fixed scene luminance).
The EV concept was developed in an attempt to simplify choosing among combinations of equivalent camera settings. Although all camera settings with the same EV nominally give the same exposure, they do not necessarily give the same picture. EV is also used to indicate an interval on the photographic exposure scale. 1 EV corresponding to a standard power-of-2 exposure step, commonly referred to as a stop
EV 0 corresponds to an exposure time of 1 sec and a relative aperture of f/1.0. If the EV is known, it can be used to select combinations of exposure time and f-number.Note EV does not equal to photographic exposure. Photographic Exposure is defined as how much light hits the camera’s sensor. It depends on the camera settings mainly aperture and shutter speed. Exposure value (known as EV) is a number that represents the exposure setting of the camera.
Thus, strictly, EV is not a measure of luminance (indirect or reflected exposure) or illuminance (incidentl exposure); rather, an EV corresponds to a luminance (or illuminance) for which a camera with a given ISO speed would use the indicated EV to obtain the nominally correct exposure. Nonetheless, it is common practice among photographic equipment manufacturers to express luminance in EV for ISO 100 speed, as when specifying metering range or autofocus sensitivity.
The exposure depends on two things: how much light gets through the lenses to the camera’s sensor and for how long the sensor is exposed. The former is a function of the aperture value while the latter is a function of the shutter speed. Exposure value is a number that represents this potential amount of light that could hit the sensor. It is important to understand that exposure value is a measure of how exposed the sensor is to light and not a measure of how much light actually hits the sensor. The exposure value is independent of how lit the scene is. For example a pair of aperture value and shutter speed represents the same exposure value both if the camera is used during a very bright day or during a dark night.
Each exposure value number represents all the possible shutter and aperture settings that result in the same exposure. Although the exposure value is the same for different combinations of aperture values and shutter speeds the resulting photo can be very different (the aperture controls the depth of field while shutter speed controls how much motion is captured).
EV 0.0 is defined as the exposure when setting the aperture to f-number 1.0 and the shutter speed to 1 second. All other exposure values are relative to that number. Exposure values are on a base two logarithmic scale. This means that every single step of EV – plus or minus 1 – represents the exposure (actual light that hits the sensor) being halved or doubled.Formulas
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ComfyDock – The Easiest (Free) Way to Safely Run ComfyUI Sessions in a Boxed Container
https://www.reddit.com/r/comfyui/comments/1j2x4qv/comfydock_the_easiest_free_way_to_run_comfyui_in/
ComfyDock is a tool that allows you to easily manage your ComfyUI environments via Docker.
Common Challenges with ComfyUI
- Custom Node Installation Issues: Installing new custom nodes can inadvertently change settings across the whole installation, potentially breaking the environment.
- Workflow Compatibility: Workflows are often tested with specific custom nodes and ComfyUI versions. Running these workflows on different setups can lead to errors and frustration.
- Security Risks: Installing custom nodes directly on your host machine increases the risk of malicious code execution.
How ComfyDock Helps
- Environment Duplication: Easily duplicate your current environment before installing custom nodes. If something breaks, revert to the original environment effortlessly.
- Deployment and Sharing: Workflow developers can commit their environments to a Docker image, which can be shared with others and run on cloud GPUs to ensure compatibility.
- Enhanced Security: Containers help to isolate the environment, reducing the risk of malicious code impacting your host machine.
