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Last updated 6/2022

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz

Language: English | Size: 1.27 GB | Duration: 5h 56m

Theory and Python

What you'll learn

Basics of Deep Learning

Artificial Neural Network

Artificial Neural Network with Keras, Python

Regression and Classification with Artificial Neural Network

Convolutional Neural Network

Recurrent Neural Network

Requirements

None

Description

Welcome to Deep Learning Fundamentals.This course covers the basic theory and Python practice of artificial neural networks. This course is designed for beginners who are interested in deep learning. Having knowledge of undergraduate level mathematics is preferable, but not a must.Artificial intelligence is a technology that makes machines imitate intelligent human behavior and human cognitive functions. Machine learning is a branch of artificial intelligence. It enables systems to learn from data automatically, that is, learn without being explicitly programmed. Deep Learning is a type of machine learning. It uses artificial neural networks to solve complex problems.One reason why deep learning has drawn much attention is that it overcomes the limitations of traditional machine learning. The first limitation is that traditional machine learning cannot handle high dimensional data. Thus, the performance of the traditional machine learning model tends to level off as the data amount increases. The second is that, when we use traditional machine learning techniques, we need to extract features manually. Therefore, when we analyze image data or movie data, traditional machine learning techniques are not suitable because such data contains a great number of features.Deep learning can overcome these limitations of traditional machine learning. An artificial neural network is one of the algorithms of artificial intelligence, and usually, it takes a form of a deep learning model. It simulates the network neurons that make up the human brain. The structure of an artificial neural network enables a deep learning model to solve complex problems that traditional machine learning algorithms can hardly handle.This course has some Python tutorials for developing deep learning models. And this course uses a library named Keras, which enables us to develop deep learning models efficiently. Basic-level Python knowledge is preferable, but Python beginners are also welcome.This course consists of three modules.1. Artificial Neural Networks2. Convolutional Neural Networks3. Recurrent Neural Networks.The first module is the basic of artificial neural network.The second module covers convolutional neural network that is a type of network effective for handling image and movie data.The third module covers recurrent neural network that is effective for time-series analysis and analyzing text data.After completing this course, you will have a fundamental knowledge of deep learning.I'm looking forward to seeing you in this course!

Overview

Section 1: Introduction

Lecture 1 Course Introduction

Lecture 2 Let's Get Started with Python!

Section 2: 1. Artificial Neural Network (Part 1) -Deep Learning Fundamentals

Lecture 3 What is Deep Learning?

Lecture 4 Artificial Neural Network

Lecture 5 Perceptron

Lecture 6 Logic Circuit

Lecture 7 Logic Gate with Python

Lecture 8 Multilayer Perceptron

Lecture 9 Multilayer Perceptron with Python

Section 3: 1. Artificial Neural Network (Part 2) -Basics of Artificial Neural Network

Lecture 10 Neural Network

Lecture 11 Activation Function

Lecture 12 Loss Function

Lecture 13 Training Neural Network

Lecture 14 Gradient Descent Method (Part 1)

Lecture 15 Gradient Descent Method (Part 2)

Lecture 16 Chain Rule

Lecture 17 Backpropagation

Lecture 18 Vanishing Gradient Problem

Lecture 19 Nonsaturating Activation Functions

Lecture 20 Parameter Initialization

Lecture 21 ANN Regression with Keras

Lecture 22 ANN Classification with Keras

Section 4: 1. Artificial Neural Network (Part 3) -Optimization & Regularization Techniques

Lecture 23 Overfitting

Lecture 24 L1 & L2 Regularization

Lecture 25 Dropout

Lecture 26 Regularization with Keras

Lecture 27 Optimizer

Lecture 28 Batch Normalization

Lecture 29 Optimization & Batch Normalization with Keras

Lecture 30 Thank You!

Section 5: 2. Convolutional Neural Network (Part 1) -CNN Basics

Lecture 31 Computer Vision

Lecture 32 Image Data

Lecture 33 What is CNN?

Lecture 34 Convolutional Layer

Lecture 35 Padding

Lecture 36 Pooling

Lecture 37 Fully-Connected Layer

Lecture 38 CNN Training Overview

Lecture 39 Image Data Augmentation

Lecture 40 Binary Image Classification with Keras

Lecture 41 Autoencoder

Section 6: 2. Convolutional Neural Network (Part 2) -Pre-Trained Model

Lecture 42 LeNet

Lecture 43 AlexNet

Lecture 44 Multiclass Classification with LeNet & AlexNet

Lecture 45 VGGNet

Lecture 46 GoogLeNet

Lecture 47 ResNet

Lecture 48 Transfer Learning

Lecture 49 Binary Classification with Transfer Learning

Section 7: 3. Recurrent Neural Network

Lecture 50 What is RNN?

Lecture 51 Structure of RNN

Lecture 52 Variable-Length Input

Lecture 53 Weight & Bias

Lecture 54 Types of RNN

Lecture 55 BPTT

Lecture 56 LSTM

Lecture 57 How LSTM work?

Lecture 58 BPTT in LSTM

Lecture 59 GRU

Lecture 60 RNN, LSTM, and GRU with Keras

Anyone who wants to start studying deep learning

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