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.
“a simple yet effective technique to estimate lighting in a single input image. Current techniques rely heavily on HDR panorama datasets to train neural networks to regress an input with limited field-of-view to a full environment map. However, these approaches often struggle with real-world, uncontrolled settings due to the limited diversity and size of their datasets. To address this problem, we leverage diffusion models trained on billions of standard images to render a chrome ball into the input image. Despite its simplicity, this task remains challenging: the diffusion models often insert incorrect or inconsistent objects and cannot readily generate images in HDR format. Our research uncovers a surprising relationship between the appearance of chrome balls and the initial diffusion noise map, which we utilize to consistently generate high-quality chrome balls. We further fine-tune an LDR difusion model (Stable Diffusion XL) with LoRA, enabling it to perform exposure bracketing for HDR light estimation. Our method produces convincing light estimates across diverse settings and demonstrates superior generalization to in-the-wild scenarios.”
