Deep Learning Programming Using Keras

You can try developing your own deep learning prediction/diagnosis program.

(2.0) 2 reviews

45 learners

seedplus1376

Python

Deep Learning(DL)

Keras

Keras Deep-learning programming

Using Keras as the front-end and TensorFlow as the back-end, you will learn from the basic theory of deep learning programming to simple examples, and apply deep learning to real-world tasks. We have planned to help you do so. You can find examples of areas where deep learning is mainly applied , such as the creation of various prediction programs, the creation of recognition-identification programs, sentence similarity identification programs, and the creation of adversarial generative neural network programs, on the web or in books. It includes not only representative examples that can be applied in practice, but also basic examples and various theoretical explanations for each. It would be helpful to listen to the lecture while downloading and executing the examples linked to each chapter. Since each lecture is about 20 minutes long and each theoretical explanation is divided into lectures, I think you will understand all the theoretical parts if you listen to the lectures until the end. So, even if it is not fun, I ask that you complete the lectures.

Learning Objectives

You can try developing a deep learning program yourself.

Helpful people

For those who want to get started with deep learning but find it difficult because of the complicated terminology and theories
For those who still don't understand even after looking through books or other examples
For those who need to develop a deep learning-based program but have difficulty finding a starting point
For those who need to understand the theoretical background, but have some practical development
Those who have developed based on TensorFlow but are new to Keras

Things to learn

Related Courses

30% discount for existing students of Data Science using Machine Learning (inquire through the consultation window)

Note

Prerequisite: Python basic grammar, Data Science with Machine Learning
Development tool: Anaconda 3.5 (with Spyder)

Introducing the knowledge sharer

Lim Hak-su

BackEnd Middleware Programmer with Perl, Java, C#, Python, GO, C/C++. NoSQL, BigData tool engineer such as Hadoop, MongoDB, Redis, ElasticSearch. DBMS administrator such as MariaDB, Oracle, MSSQL. ERC20 based token developer. Machine Learning developer. (Python, Go-based Social Crawling, A/B Testing, ML-based data analysis tool) Inflearn ' Data Science with Machine Learning ' lecture

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Curriculum

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23 lectures ∙ (6hr 2min)

Section 1. Data

1 lectures

1. Download data

Section 2. Chapter 1. Basic Theory

7 lectures ∙ (1hr 40min)

2. Deep Learning Glossary
16:00
3. Precision, Sensitivity
13:00
4. Perceptron Basics
13:00
5. Perceptron Advanced
11:00
6. Multiperceptron
19:00
7. Activation function
14:00
8. Activation function and dataset
14:00

Section 3. Chapter 2. Practice

0 lectures

Section 4. Prediction program

5 lectures ∙ (1hr 36min)

Section 5. Recognition Identification Program

4 lectures ∙ (1hr 11min)

Section 6. Sentence similarity identification program

4 lectures ∙ (1hr 10min)

Section 7. Generative Adversarial Networks (GANs)

2 lectures ∙ (25min)

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2 reviews

2.0