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
→ Midjourney: for the stunning visuals and style. → Nano Banana: for camera angles and edits. → Seedance + Kling 2.5: for motion and animation. → Suno: for the music track in one go. → Elevenlabs: for sound effects.
I created a million-dollar Pixar-quality short in just 8 days using AI.
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.
In color technology, color depth also known as bit depth, is either the number of bits used to indicate the color of a single pixel, OR the number of bits used for each color component of a single pixel.
When referring to a pixel, the concept can be defined as bits per pixel (bpp).
When referring to a color component, the concept can be defined as bits per component, bits per channel, bits per color (all three abbreviated bpc), and also bits per pixel component, bits per color channel or bits per sample (bps). Modern standards tend to use bits per component, but historical lower-depth systems used bits per pixel more often.
Color depth is only one aspect of color representation, expressing the precision with which the amount of each primary can be expressed; the other aspect is how broad a range of colors can be expressed (the gamut). The definition of both color precision and gamut is accomplished with a color encoding specification which assigns a digital code value to a location in a color space.
