Introduction

What is Deep Learning

Introduction to Neural Networks

How do Neural Networks LEARN?

Core terminologies used in Deep Learning

Activation Functions

Loss Functions

Optimizers

Parameters vs Hyperparameters

Epochs, Batches & Iterations

Conclusion to Terminologies

Introduction to Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Regularization

Introduction to Neural Network Architectures

Fully-Connected Feedforward Neural Nets

Recurrent Neural Nets

Convolutional Neural Nets

Introduction to the 5 Steps to EVERY Deep Learning Model

1. Gathering Data

2. Preprocessing the Data

3. Training your Model

4. Evaluating your Model

5. Optimizing your Model's Accuracy

Conclusion to the Course

freeCodeCamp.org

Learn the fundamental concepts and terminology of Deep Learning, a sub-branch of Machine Learning. This course is designed for absolute beginners with no experience in programming. You will learn the key ideas behind deep learning without any code.

You'll learn about Neural Networks, Machine Learning constructs like Supervised, Unsupervised and Reinforcement Learning, the various types of Neural Network architectures, and more.

✏️ Course developed by Jason Dsouza. Check out his YouTube channel: http://youtube.com/jasmcaus

⭐️ Course Contents ⭐️
⌨️ (0:00) Introduction
⌨️ (1:18) What is Deep Learning
⌨️ (5:25) Introduction to Neural Networks
⌨️ (6:12) How do Neural Networks LEARN?
⌨️ (12:06) Core terminologies used in Deep Learning
⌨️ (12:11) Activation Functions
⌨️ (22:36) Loss Functions
⌨️ (23:42) Optimizers
⌨️ (30:10) Parameters vs Hyperparameters
⌨️ (32:03) Epochs, Batches & Iterations
⌨️ (34:24) Conclusion to Terminologies
⌨️ (35:18) Introduction to Learning
⌨️ (35:34) Supervised Learning
⌨️ (40:21) Unsupervised Learning
⌨️ (43:38) Reinforcement Learning
⌨️ (46:25) Regularization
⌨️ (51:25) Introduction to Neural Network Architectures
⌨️ (51:37) Fully-Connected Feedforward Neural Nets
⌨️ (54:05) Recurrent Neural Nets
⌨️ (1:04:40) Convolutional Neural Nets
⌨️ (1:08:07) Introduction to the 5 Steps to EVERY Deep Learning Model
⌨️ (1:08:23) 1. Gathering Data
⌨️ (1:11:27) 2. Preprocessing the Data
⌨️ (1:19:05) 3. Training your Model
⌨️ (1:19:33) 4. Evaluating your Model
⌨️ (1:19:55) 5. Optimizing your Model's Accuracy
⌨️ (1:25:15) Conclusion to the Course

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Deep Learning Crash Course for Beginners

Deep Learning Crash Course for Beginners

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