Deep Learning Fundamentals

Deep Learning Fundamentals, available at $49.99, has an average rating of 4.65, with 60 lectures, based on 33 reviews, and has 5088 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who wants to start studying deep learning It is particularly useful for Anyone who wants to start studying deep learning.

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Summary

Title: Deep Learning Fundamentals

Price: $49.99

Average Rating: 4.65

Number of Lectures: 60

Number of Published Lectures: 60

Number of Curriculum Items: 60

Number of Published Curriculum Objects: 60

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Anyone who wants to start studying deep learning

Target Audiences

Anyone who wants to start studying deep learning

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 Networks

2. Convolutional Neural Networks

3. 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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction

Lecture 2: Lets Get Started with Python!

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

Lecture 1: What is Deep Learning?

Lecture 2: Artificial Neural Network

Lecture 3: Perceptron

Lecture 4: Logic Circuit

Lecture 5: Logic Gate with Python

Lecture 6: Multilayer Perceptron

Lecture 7: Multilayer Perceptron with Python

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

Lecture 1: Neural Network

Lecture 2: Activation Function

Lecture 3: Loss Function

Lecture 4: Training Neural Network

Lecture 5: Gradient Descent Method (Part 1)

Lecture 6: Gradient Descent Method (Part 2)

Lecture 7: Chain Rule

Lecture 8: Backpropagation

Lecture 9: Vanishing Gradient Problem

Lecture 10: Nonsaturating Activation Functions

Lecture 11: Parameter Initialization

Lecture 12: ANN Regression with Keras

Lecture 13: ANN Classification with Keras

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

Lecture 1: Overfitting

Lecture 2: L1 & L2 Regularization

Lecture 3: Dropout

Lecture 4: Regularization with Keras

Lecture 5: Optimizer

Lecture 6: Batch Normalization

Lecture 7: Optimization & Batch Normalization with Keras

Lecture 8: Thank You!

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

Lecture 1: Computer Vision

Lecture 2: Image Data

Lecture 3: What is CNN?

Lecture 4: Convolutional Layer

Lecture 5: Padding

Lecture 6: Pooling

Lecture 7: Fully-Connected Layer

Lecture 8: CNN Training Overview

Lecture 9: Image Data Augmentation

Lecture 10: Binary Image Classification with Keras

Lecture 11: Autoencoder

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

Lecture 1: LeNet

Lecture 2: AlexNet

Lecture 3: Multiclass Classification with LeNet & AlexNet

Lecture 4: VGGNet

Lecture 5: GoogLeNet

Lecture 6: ResNet

Lecture 7: Transfer Learning

Lecture 8: Binary Classification with Transfer Learning

Chapter 7: 3. Recurrent Neural Network

Lecture 1: What is RNN?

Lecture 2: Structure of RNN

Lecture 3: Variable-Length Input

Lecture 4: Weight & Bias

Lecture 5: Types of RNN

Lecture 6: BPTT

Lecture 7: LSTM

Lecture 8: How LSTM work?

Lecture 9: BPTT in LSTM

Lecture 10: GRU

Lecture 11: RNN, LSTM, and GRU with Keras

Instructors

Takuma Kimura
Scientist of Organizational Behavior & Business Analytics

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5 stars: 25 votes

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