IntelliJ IDEA for Beginners - Learn IntelliJ IDEA from Scratch

Complete IntelliJ IDEA Course for Beginners

IntelliJ IDEA for Beginners - Learn IntelliJ IDEA from Scratch

How to become an expert IntelliJ IDEA Developer

This course takes is specifically designed for developers that either don’t have any previous experience using an Integrated Development Environment (IDE) tool or experienced IDE developers new to Intellij IDEA (e.g. experienced Eclipse developers).

With that said, even if you’re an experienced Intellij IDEA developer you might still get something out of the course. This is specially true since this course is (almost exclusively) based on version 2019 which introduced features that you may not yet me familiar with. So please still check it out!

Also, no particular programming language knowledge is assumed since Intellij IDEA is pretty much language agnostic and supports many different programming languages.

Sections I, II and III are mandatory and must be taken in order.

Section IV is specifically designed for experienced Eclipse developers interested in a quick migration path to IntelliJ

The rest of the sections (V, VI, VII and VIII) can be taken in any order as they’re self contained. I highly recommend that you still taken them all, particularly if you don’t have any previous Intellij IDEA experience.

#java #intellij-idea #intellij #web-development

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IntelliJ IDEA for Beginners - Learn IntelliJ IDEA from Scratch

Complete IntelliJ IDEA Course for Beginners

IntelliJ IDEA for Beginners - Learn IntelliJ IDEA from Scratch

How to become an expert IntelliJ IDEA Developer

This course takes is specifically designed for developers that either don’t have any previous experience using an Integrated Development Environment (IDE) tool or experienced IDE developers new to Intellij IDEA (e.g. experienced Eclipse developers).

With that said, even if you’re an experienced Intellij IDEA developer you might still get something out of the course. This is specially true since this course is (almost exclusively) based on version 2019 which introduced features that you may not yet me familiar with. So please still check it out!

Also, no particular programming language knowledge is assumed since Intellij IDEA is pretty much language agnostic and supports many different programming languages.

Sections I, II and III are mandatory and must be taken in order.

Section IV is specifically designed for experienced Eclipse developers interested in a quick migration path to IntelliJ

The rest of the sections (V, VI, VII and VIII) can be taken in any order as they’re self contained. I highly recommend that you still taken them all, particularly if you don’t have any previous Intellij IDEA experience.

#java #intellij-idea #intellij #web-development

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

ML Optimization pt.1 - Gradient Descent with Python

So far in our journey through the Machine Learning universe, we covered several big topics. We investigated some regression algorithms, classification algorithms and algorithms that can be used for both types of problems (SVM**, **Decision Trees and Random Forest). Apart from that, we dipped our toes in unsupervised learning, saw how we can use this type of learning for clustering and learned about several clustering techniques.

We also talked about how to quantify machine learning model performance and how to improve it with regularization. In all these articles, we used Python for “from the scratch” implementations and libraries like TensorFlow, Pytorch and SciKit Learn. The word optimization popped out more than once in these articles, so in this and next article, we focus on optimization techniques which are an important part of the machine learning process.

In general, every machine learning algorithm is composed of three integral parts:

1. A loss function.
2. Optimization criteria based on the loss function, like a cost function.
3. Optimization technique – this process leverages training data to find a solution for optimization criteria (cost function).

As you were able to see in previous articles, some algorithms were created intuitively and didn’t have optimization criteria in mind. In fact, mathematical explanations of why and how these algorithms work were done later. Some of these algorithms are Decision Trees and kNN. Other algorithms, which were developed later had this thing in mind beforehand. SVMis one example.

During the training, we change the parameters of our machine learning model to try and minimize the loss function. However, the question of how do you change those parameters arises. Also, by how much should we change them during training and when. To answer all these questions we use optimizers. They put all different parts of the machine learning algorithm together. So far we mentioned Gradient Decent as an optimization technique, but we haven’t explored it in more detail. In this article, we focus on that and we cover the grandfather of all optimization techniques and its variation. Note that these techniques are not machine learning algorithms. They are solvers of minimization problems in which the function to minimize has a gradient in most points of its domain.

Dataset & Prerequisites

Data that we use in this article is the famous Boston Housing Dataset . This dataset is composed 14 features and contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It is a small dataset  with only 506 samples.

For the purpose of this article, make sure that you have installed the following _Python _libraries:

• **NumPy **– Follow this guide if you need help with installation.
• **SciKit Learn **– Follow this guide if you need help with installation.
• Pandas – Follow this guide if you need help with installation.

Once installed make sure that you have imported all the necessary modules that are used in this tutorial.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import SGDRegressor

Apart from that, it would be good to be at least familiar with the basics of linear algebra, calculus and probability.

Why do we use Optimizers?

Note that we also use simple Linear Regression in all examples. Due to the fact that we explore optimizationtechniques, we picked the easiest machine learning algorithm. You can see more details about Linear regression here. As a quick reminder the formula for linear regression goes like this:

where w and b are parameters of the machine learning algorithm. The entire point of the training process is to set the correct values to the w and b, so we get the desired output from the machine learning model. This means that we are trying to make the value of our error vector as small as possible, i.e. to find a global minimum of the cost function.

One way of solving this problem is to use calculus. We could compute derivatives and then use them to find places where is an extrema of the cost function. However, the cost function is not a function of one or a few variables; it is a function of all parameters of a machine learning algorithm, so these calculations will quickly grow into a monster. That is why we use these optimizers.

#ai #machine learning #python #artificaial inteligance #artificial intelligence #batch gradient descent #data science #datascience #deep learning #from scratch #gradient descent #machine learning #machine learning optimizers #ml optimization #optimizers #scikit learn #software #software craft #software craftsmanship #software development #stochastic gradient descent

10 Ways Stand Out as a Java Developer and Land that Dream Job

Java is has been one of the most popular programming languages for decades. The number of specialists who want to become proficient in Java is rapidly growing. Because the competition is fierce, it’s no longer enough to just be a good Java developer — you need to acquire deep knowledge and get familiar with many concepts to be ahead of the competition.

If you’re the one who’s stuck asking yourself “What should I learn to stand out as a Java developer?”, this blog post can help you figure things out.

1. Get a Solid Base and Clear Idea of OOP Principles

2. Read Books That Cover Those Principles

3. Get Familiar with the Spring Framework

4. Learn the Most Essential APIs and Libraries

5. Get Deep Knowledge of Java 11

6. Focus on JVM and its Internals

7. Have Multiple Methodologies at Hand

8. Get Used to Automated Testing

9. Polish Up Your Coding Skills

#java #learn-java #java-development-resources #learning-to-code #learn-to-code #beginners #beginners-guide #learn-to-code-java

12 Common Java Mistakes Made by Newcomers

Everyone makes mistakes, not just beginners, but even professionals. This article goes over a dozen common mistakes that Java newbies and newcomers make and how to avoid them. Have you or your colleagues made any of these common Java mistakes early in your career?

Everyone makes mistakes, not only learners or beginners but professionals. As a programming course, the CodeGym team often collects mistakes of newbies to improve our auto validator. This time we decided to interview experienced programmers about mistakes in Java they made closer to their careers start or noticed them among their young colleagues.

We collected their answers and compiled this list of dozen popular mistakes Java beginners make. The order of errors is random and does not carry any special meaning.

#java #learn-java #java-programming #beginners #beginners-to-coding #learning-to-code #learn-to-code #learn-to-code-java