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Deep Learning Complete Course | Part 1| ANN implementation.
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This first part of a deep learning series spends its time on the ideas most tutorials skip: what deep learning is, how perceptrons work, and why neural networks train at all. It is a long session, and the code and notes are linked from its description.
Newcomers building a first artificial neural network, and people refreshing the basics before moving on to convolutional networks, will find this useful. The loss function and optimizer segments deserve close attention. The video ends with a comparison of black box and white box models.
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
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