Oct

08

2020

Deep Learning in Practice I: Basics and Dataset Design

supnatural 8 Oct 2020 02:44 LEARNING » e-learning - Tutorial

Deep Learning in Practice I: Basics and Dataset Design
Deep Learning in Practice I: Basics and Dataset Design
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 48000 Hz
Language: English | VTT | Size: 2.49 GB | Duration: 4.5 hours

What you'll learn
Develop complex deep learning projects
Efficiently organize and structure deep learning projects
Develop reusable libraries to reduce development time of deep learning projects
Understand how to perform efficient training of classification projects
Evaluate the performance of deep learning models
Load datasets in numpy array in different ways
Conduct training on local machine and Google Colab
Design a dataset from data collection to HDF5 partitioned dataset
Requirements
Understand the basic concepts of machine learning (recommended, but not required)
Be familiar with Python programming language and data structures (Numpy, Pandas)
Understand the basic concepts of neural networks (recommended, but not required)
Description
You want to start developing deep learning solutions, but you do not want to lose time in mathematics and theory?

You want to conduct deep learning projects, but do not like the hassle of tedious programming tasks?

Do you want an automated process for developing deep learning solutions?

This course is then designed for you! Welcome to Deep Learning in Practice, with NO PAIN!

This course is the first course on a series of Deep Learning in Practice Courses of Anis Koubaa, namely

Deep Learning in Practice I: Basics and Dataset Design (this course): the student will learn the basics of conducting a classification project using deep neural networks, then he learns about how to design a dataset for industrial-level professional deep learning projects.

Deep Learning in Practice II: Transfer Learning and Models Evaluation (to release on August 2020): the student will learn how to manage complex deep learning projects and develop models using transfer learning using several state-of-the-art CNN algorithms. He will learn how to develop reusable projects and how to compare the results of different deep learning models in an automated manner.

Deep Learning in Practice III: Deployment of Deep Learning Models (to release on September 2020): the student will learn how to deploy deep learning models in a production environment. We will present the deployment techniques used in industry such as Flask, Docker, Tensorflow Serving, Tensorflow jаvascript, and Tensorflow Lite, for deployment in a different environment. Despite important, this topic has little coverage in tutorials and documentations.

Deep Learning in Practice I: Basics and Dataset Design

There are plenty of courses and tutorials on deep learning. However, some practical skills are challenging to find in this massive bunch of deep learning resources, and that someone would spend a lot of time to get these practical skills.

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http://rapidgator.net/file/4ed323364af3d0363c73da41eb955455/Deep_Learning_in_Practice_I_Basics_and_Dataset_Design.part2.rar.html
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https://uploadgig.com/file/download/E1Ea92d5D28f9933/Deep%20Learning%20in%20Practice%20I%20Basics%20and%20Dataset%20Design.part2.rar
https://uploadgig.com/file/download/Fe9947711F71E648/Deep%20Learning%20in%20Practice%20I%20Basics%20and%20Dataset%20Design.part3.rar

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