Complete Machine Learning Course with Projects | Learn ML Step-by-Step - Deep Learning Complete Course | Part 1| ANN implementation.

Instructor – Akarsh VyasWelcome to the first step of your Deep Learning journey!In this video, we’ll dive into the complete foundation of neural networks, covering everything you must know before building your first ANN or CNN. 📂 You can download the code and datasets from here:Code Link – https://github.com/AkarshVyas/Deep_learning_video 📘 All the notes of our classes are here:Notes – https://drive.google.com/file/d/1sZhNaqK428laMp_vhBhzZCpuSqELXhDM/view?usp=sharing Here’s what you’ll learn: * What Deep Learning really is (and how it differs from Machine Learning) * The intuition behind Perceptrons & ANN * Key building blocks: Activation Functions, Loss Functions, and Optimizers * Forward Propagation explained step by step * Backward Propagation with real intuition * A quick hands-on demo project in TensorFlow/Keras These are the most critical and often skipped steps in Deep Learning — but they are what make your neural networks actually work. Whether you’re just starting out or refreshing your basics, this session will give you the clarity you need for real-world AI projects. 🚀 Start here. Build smarter. 00:00:00 - 00:00:57 - Introduction 00:00:57 - 00:12:15 - Basics 00:12:15 - 00:29:53 - Perceptrons 00:29:53 - 00:48:54 - Forward Propogation 00:48:54 - 01:09:22 - Backward Propogation 01:09:22 - 01:23:55 - Vanishing Gradient Problem 01:23:55 - 01:50:48 - Activation Functions 01:50:48 - 02:20:11 - Basic Code for a Model 02:20:11 - 03:01:29 - Loss Functions 03:01:29 - 03:43:54 - Optimizers 03:43:54 - 04:16:25 - ANN Project 04:16:25 - 04:18:47 - Black Box vs White Box model 04:18:47 - 04:20:34 - Outro