Learn Spring 5 - An Ultimate Guide to Spring 5 Core

Java — Mon, 06 Jan 2020 08:30:02 +0000

Learn Spring 5 - An Ultimate Guide to Spring 5 Core

A comprehensive guide on Spring fundamentals.

Whether you are a complete beginner on Spring Framework or an experience Spring developer, there are so many take ways from this course.

It covers the following Spring concepts:
Module 1: Course Overview

Introduction
What we need
Target Audience
Prerequisite
Course Walk through
What won’t be covered
Module summary

Module 2: Set up the development Environment

Introduction
Installing Java
Installing Maven
Installing - Spring Tool Suite
Creating Maven Project in Spring Tool Suite
Installing - Intellij IDEA
Creating Maven Project in Intellij Idea

Module 3: Spring Core Introduction

Spring Overview
Spring History
Spring Modules
Dependency Management: Traditional Ways
Dependency Management: Dependency Injection
Concepts of POJO and Bean
Inversion of Control : Spring IOC Container
Bean Factory Interface
ApplicationContext Interface
Configuring beans: XML based, Annotation and Java Config configuration
Module conclusion

Module 4: XML Based configuration

Introduction
Configuring POJOs with XML based configuration
ClassPathXmlApplicationContext
Setter based dependency Injection
Constructor based dependency Injection
Constructor vs Setter based Injections
Circular dependencies
Composing XML-based Configuration Metadata
Referencing Other beans
Inner Beans
Handling Java Collections
Merging Collections
Depends-on
Lazy-Initialization

Module 5: Autowiring

Basics of Autowiring
Autowiring Types,
No Autowiring ,
Autowiring byName,
Autowiring byType
Autowiring constructor
Issues with Autowiring

Module 6: Java Configuration

Configuring beans with Java Configuration
Component scan with @ComponentScan
@Configuration and @Bean Annotation
@Component, @Configuration, @Service and @Repository annotations
Constructor Injection
Autowiring POJOs
@Primary Annotation
@Qualifier Annotation
Importing Configuration
@Scope Annotation
@Lazy Annotation
@DependsOn

Module 7: Spring Beans in Depth

Introduction
Bean Life Cycle Callbacks
@PostConstruct and @PreDestroy annotations
Bean Scope
Bean Post Processor
POJO Creation with factory methods - Static factory, instance method and Spring factory bean
Managing environments - @profile annotation
Spring Aware Interfaces
BeanNameAware
BeanFactoryAware
ApplicationContextAware
MessageSourceAware
ResourceLoaderAware
EnvironmentAware

Module 8: Spring Aspect Oriented Programming

AOP Introduction
Core AOP Concepts
What is Aspect?
Join Point
What is Advice?
Pointcut
AOP Proxy
Aspectj
Weaving
Aspectj and @EnableAspectJAutoProxy annotation
Advice Annotations: @Before, @After, @AfterReturing, @AfterThrowing, @Around
@Before and @After Annotation
@AfterReturning Annotation
@AfterThrowing Annotation
@Around
Demostration

Module 9: Spring Task Executor

Introduction to Java Executor, ExecutorService
Runnable, Callable and Future
Threadpool - Fixed thread pool , Cached thread pool, Single thread executor, scheduled thread pool executor
Spring TaskExecutor
Spring SimpleAsyncTaskExecutor
Spring SyncTaskExecutor
Spring TaskExecutorAdapter

Module 10: Communication between Beans - ApplicationEvent Management

Introduction
ApplicationListener Interface
ApplicationEventPublisher
Example

Module 11: Conclusion

Source Code
Course Aummary
WhatsNext

What you'll learn

Spring Framework Core Concepts
Spring History
Environment Set up in STS and Intellij Idea
Spring Dependency Injection
Configuring Spring IoC Container through XML configuration
Configuring Spring IoC Container through Java annotation configuration
Spring Bean Autowiring - ByName, ByType, Constructor
Spring Bean Life Cycle Callbacks, Bean Post Processor,
...

Top 5 TensorFlow and Machine Learning Courses for Programmers

Java — Mon, 30 Dec 2019 07:16:01 +0000

TensorFlow is a Python-friendly open source library for numerical computation that makes machine learning faster and easier

Machine learning is a complex discipline. But implementing machine learning models is far less daunting and difficult than it used to be, thanks to machine learning frameworks—such as Google’s TensorFlow—that ease the process of acquiring data, training models, serving predictions, and refining future results.

Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. TensorFlow bundles together a slew of machine learning and deep learning (aka neural networking) models and algorithms and makes them useful by way of a common metaphor. It uses Python to provide a convenient front-end API for building applications with the framework, while executing those applications in high-performance C++.

One of my goals in 2018 is to explore Machine Learning and Data Science and that's why I am learning Python as well. You might have seen a couple of python posts like best courses to learn Python in this blog, which I have been using to learn Python this year.

Python has a lot of very useful and popular Machine Learning APIs, e.g. TensorFlow, NumPy, Pandas, Sci-Kit, etc., but I am mostly focusing on TensorFlow at this moment because of an obvious reason: it's supported by Google.

In this article, I am going to share about some of the best online courses to learn TensorFlow and Machine Learning for programmers.

Top 5 TensorFlow and Machine Learning Courses

All these courses not only teach theoretical knowledge but also practical stuff which you need to gain some hands-on experience.

1. Complete Guide to TensorFlow for Deep Learning with Python

This is probably the most popular TensorFlow course on Udemy, This course is taught by Jose Portilla with more than 71,252 students enrolled and thousands of 4.5 rating. It covers most of the things about TensorFlow and you can take it even if you are new to Machine Learning.

The first couple of chapters will explain Machine Learning and how TensorFlow can solve real-world problems through its neural networks.

In this course, you will learn about how Neural Networks work and how you can use TensorFlow for classification and regression tasks, e.g. image classification with Convolutional Neural Networks.

You will also learn about how to build your own Neural Network from scratch using Python and perform time series analysis with Recurrent Neural Networks. Overall, it is a great course to learn the basics of TensorFlow with Python.

2. Deep Learning with TensorFlow 2.0

This is another great course to learn TensorFlow on Udemy. If you are a first-timer, this is probably the best course because it will generate your interest in the complex but exciting world of Data Science, Machine Learning, and Deep learning.

The course starts with basics but runs really deep. It's great for Python developers as the course focuses on TenosorFlow with Python. It also teaches you NumPy, which is another popular Machine Learning API.

The best part of this course is that you will create your own Machine Learning algorithm after learning the basics of TensorFlow and Machine Learning.

It's business-focused, which means you will learn Machine Learning with real-world scenarios, e.g. how to use Machine Learning to optimize business performance, retain customers, improve conversions, etc, all by leveraging the real data acquired by real companies.

3. TensorFlow: Getting Started

This is another great course on Pluralsight . This course is taught by Jerry Kurata - This course shows you how to install and use TensorFlow, a leading machine learning library from Google. You'll see how TensorFlow can create a range of machine learning models, from simple linear regression to complex deep neural networks.

4. Complete Tensorflow 2 and Keras Deep Learning Bootcamp

This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand.

This course will focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!

5. A beginners guide for building neural networks in tensorflow

This is a really short course that will teach you neural networks and TensorFlow in less than 3 hours. It's kind of a crash course on TensorFlow and Neural networks.

You will start from scratch by learning the basic syntax of TensorFlow, Google's Machine Learning framework, and later build a neural network in Python code.

The best part of this course is that it also explains how you can train and test your neural network, the most interesting part of doing any Deep Learning project.

Conclusion

That's all about some of the best online courses to learn TensorFlow and Machine Learning using Python. TensorFlow is a very powerful Machine Learning API that has several real-world usages. For example, you can use it to detect images like you can take pictures of number plates and then guess the number and find car owner details from the registration database. Speed cameras in the highway can have this program installed to detect and send notice to speeding car owners before they reach home. If you need more general courses, you can also check out my earlier post about deep learning courses.

DEV Community: Java

Learn Spring 5 - An Ultimate Guide to Spring 5 Core