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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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9 Best Hacks to Make a Cinematic Video with Any Camera
https://www.flexclip.com/learn/cinematic-video.html
- Frame Your Shots to Create Depth
- Create Shallow Depth of Field
- Avoid Shaky Footage and Use Flexible Camera Movements
- Properly Use Slow Motion
- Use Cinematic Lighting Techniques
- Apply Color Grading
- Use Cinematic Music and SFX
- Add Cinematic Fonts and Text Effects
- Create the Cinematic Bar at the Top and the Bottom
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Generative Detail Enhancement for Physically Based Materials
https://arxiv.org/html/2502.13994v1
https://arxiv.org/pdf/2502.13994
A tool for enhancing the detail of physically based materials using an off-the-shelf diffusion model and inverse rendering.
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Google – Artificial Intelligence free courses
1. Introduction to Large Language Models: Learn about the use cases and how to enhance the performance of large language models.
https://www.cloudskillsboost.google/course_templates/5392. Introduction to Generative AI: Discover the differences between Generative AI and traditional machine learning methods.
https://www.cloudskillsboost.google/course_templates/5363. Generative AI Fundamentals: Earn a skill badge by demonstrating your understanding of foundational concepts in Generative AI.
https://www.cloudskillsboost.google/paths4. Introduction to Responsible AI: Learn about the importance of Responsible AI and how Google implements it in its products.
https://www.cloudskillsboost.google/course_templates/5545. Encoder-Decoder Architecture: Learn about the encoder-decoder architecture, a critical component of machine learning for sequence-to-sequence tasks.
https://www.cloudskillsboost.google/course_templates/5436. Introduction to Image Generation: Discover diffusion models, a promising family of machine learning models in the image generation space.
https://www.cloudskillsboost.google/course_templates/5417. Transformer Models and BERT Model: Get a comprehensive introduction to the Transformer architecture and the Bidirectional Encoder Representations from the Transformers (BERT) model.
https://www.cloudskillsboost.google/course_templates/5388. Attention Mechanism: Learn about the attention mechanism, which allows neural networks to focus on specific parts of an input sequence.
https://www.cloudskillsboost.google/course_templates/537
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Photography basics: Color Temperature and White Balance
Color Temperature of a light source describes the spectrum of light which is radiated from a theoretical “blackbody” (an ideal physical body that absorbs all radiation and incident light – neither reflecting it nor allowing it to pass through) with a given surface temperature.
https://en.wikipedia.org/wiki/Color_temperature
Or. Most simply it is a method of describing the color characteristics of light through a numerical value that corresponds to the color emitted by a light source, measured in degrees of Kelvin (K) on a scale from 1,000 to 10,000.
More accurately. The color temperature of a light source is the temperature of an ideal backbody that radiates light of comparable hue to that of the light source.
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