Forem: Rodolfo Mendes

3 CLI Tools Every Java Developer Must Learn

Rodolfo Mendes — Fri, 09 Sep 2022 11:54:53 +0000

1 - java

This is the essential tool that every serious Java developer must master. The java command is part of the Java SDK and invokes the JVM (Java Virtual Machine). This command is responsible for running Java programs compiled into Java bytecode. The java command is the heart of the Java platform. By practicing the java command, you can learn how to invoke a java program through a .class file or .jar file, how to setup the classpath for the JVM, how to setup the size of the heap space, how can you set different modes of execution of the garbage collector or even how can you configure the JVM to dump the memory to a file in the moment when it crashes. These skills make an enormous difference when running systems in production.

2 - javac

The javac command is also part of the Java SDK and it invokes the Java compiler, used to compile a set of Java source code files into a set of Java bytecode files.

3 - Maven

Building and compiling simple application using the javac command is quite simple. However, for bigger application the process of building and packaging an application is much more complex. For this task you can use Maven. Although Maven is not part of Java specification, the Java community has widely adopted Maven for dependency management, build and package Java applications.

5 CLI Tools Every Developer Must Learn

Rodolfo Mendes — Fri, 09 Sep 2022 11:51:26 +0000

If you are a beginner developer, this is a good starting point for your studies. If you are a senior already and you are not familiar with these tools, would be a good moment to step back a while and improve some fundamentals.

1 - Linux terminal

You don't necessarily need to create a dual boot on your computer to learn Linux terminal. MacOS and Linux have similar terminal commands and for Windows users there are alternatives like Git bash or WLS. But as developer, you need to be familiar with Linux terminal commands, at least, the basic ones like navigating the file system, listing processes, creating links, creating and listing environment variables, setting permissions and executing files. Standard input and output redirection and processes pipelining would be a good catch too.

2 - man pages

Linux man (man stands for MANual) pages could be part of the topic 1 but deserves a special place. Back to 1970s, when UNIX was created, computer operators needed a way to check how a command worked. And because they could not memorize everything and there was no Google nor Stackoverflow, the command man was created. So, it's a quite simple command that returns the documentation of the referenced command on the terminal. Creating the habit of reading the man pages leads you to quickly master using the Linux terminal

3 - ssh

Most of time we develop in network environments, and we need to do it securely. So, you need to know how to create an encrypted connection to another computer over a network so you can operate it via terminal.

4 - git

Although most of IDEs have Git tooling integrated, I believe that going through CLI at least during your learning process can get you deeper on how the Git versioning process works. Also, code checkout is the first step during CI/CD pipelines, so you will need to understand how the CLI works to build that automation.

5 - bash/ksh/powershell

Finally, some basic knowledge of the scripting language of your favorite OS can help put all this together and then you'll be able to automate some daily tasks and be more productive.

Why Developers Should Learn CLI Tools

Rodolfo Mendes — Fri, 09 Sep 2022 11:42:21 +0000

Nowadays, using CLI (Command Line Interfaces) to perform programing tasks may look antiquated, and although modern graphical IDEs can represent a gain of productivity in our daily routines, I honestly believe that developers should master using CLI tools. Specially those who are initiating in the profession or when we are learning a new language or tool. And these are few of my reasons to do so:

1 - By using a CLI allows you to get into details. For example, using mvn or dotnet commands to build your Java or C# application requires from you to go deep on the cycle build-test-run of an application. It also forces you to explore error messages and parameters when things go wrong. This kind of effort during your learning process increases your problem-solving capacity.

2 - CLIs are well-suited for automation. If you are familiar with CLIs and do a lot of repetitive and tedious tasks using commands, then you can easily automate them with the help of some scripting language like Bash or PowerShell.

Finally, my idea is not to tell you to just abandon IDEs or GUIs. They are great for visualization and for quick and ad-hoc tasks. But for our developer and SREs world, CLIs still play a crucial role and they mandatory in our toolboxes.

Building a Machine Learning Model: Collecting Data

Rodolfo Mendes — Fri, 20 Aug 2021 18:35:12 +0000

Data is the main ingredient of Machine Learning models.

An actual Machine Learning project starts with the team assessing if the necessary data is available. If not (which would be no surprise), the team needs to develop a strategy to collect and store the necessary data before modeling Machine Learning Algorithms.

Common sources of data … Continue reading Building a Machine Learning Model: Collecting Data →

Types of Machine Learning: Deep Learning

Rodolfo Mendes — Fri, 06 Aug 2021 22:12:56 +0000

Deep Learning is a branch of Machine Learning specialized in Artificial Neural Networks with multiple intermediary layers.

Neural Networks with multiple layers can approximate very complex non-linear functions.

That’s why Deep Learning has been very successful in CV (Computer Visions), NLP (Natural Language Processing), and Reinforcement Learning.

Deep Learning architectures include: Convolutional Neural Networks Recurrent … Continue reading Types of Machine Learning: Deep Learning →

Types of Machine Learning: Time Series Analysis

Rodolfo Mendes — Tue, 03 Aug 2021 14:10:54 +0000

The typical regression task predicts the value of a target variable based on the values of one or more feature variables.

For example, to predict the price of a house based on its characteristics like size, number of rooms, etc. But in some cases, we want to predict the value of a variable based on … Continue reading Types of Machine Learning: Time Series Analysis →

Types of Machine Learning: Recommender Systems

Rodolfo Mendes — Sun, 25 Jul 2021 09:51:48 +0000

Recommender systems are a category of Machine Learning algorithms that predict a user’s rating or preferences over a collection of items.

Recommender systems algorithms include: Collaborative filtering Content-based filtering Session-based Session-based recommender systems Reinforcement learning for recommender systems include: Multi-criteria recommender systems Risk-aware recommender systems Mobile recommender systems

Types of Machine Learning: Reinforcement Learning

Rodolfo Mendes — Fri, 16 Jul 2021 10:40:06 +0000

In the reinforcement learning paradigm, the learning process is a loop in which the agent reads the state of the environment and then executes an action.

Then the environment returns its new state and a reward signal, indicating if the action was correct or not.

The process continues until the environment reaches a terminal condition … Continue reading Types of Machine Learning: Reinforcement Learning →

Machine Learning Applications: Topic Discovery with Clustering

Rodolfo Mendes — Fri, 09 Jul 2021 11:07:34 +0000

Given a set of documents, a common task is to group them accordingly to topics or subjects.

A human agent can create a hierarchy of subjects and assign each document to its related issue. However, a clustering algorithm can create this structure automatically and more precisely.

We can apply hierarchical clustering algorithms to group documents … Continue reading Machine Learning Applications: Topic Discovery with Clustering →

Exploratory Data Analysis for Machine Learning

Rodolfo Mendes — Fri, 02 Jul 2021 11:12:53 +0000

Exploratory data analysis is the most challenging task when building a machine learning model, especially for beginners.

A result of the No-Free-Lunch-Theorem is that there’s no single model that will perform well for every dataset.

In other words, there’s no silver bullet Machine Learning Algorithm.

The practical consequence is that we need to make a … Continue reading Exploratory Data Analysis for Machine Learning →

Machine Learning Applications: Stroke Prediction

Rodolfo Mendes — Fri, 25 Jun 2021 14:35:53 +0000

The Stroke Prediction Dataset at Kaggle is an example of how to use Machine Learning for disease prediction.

The dataset comprises more than 5,000 observations of 12 attributes representing patients’ clinical conditions like heart disease, hypertension, glucose, smoking, etc.

For each instance, there’s also a binary target variable indicating if a patient had a stroke.

We can build a … Continue reading Machine Learning Applications: Stroke Prediction →

Machine Learning Applications: Medical Diagnosis

Rodolfo Mendes — Fri, 18 Jun 2021 10:52:45 +0000

An increasing application of machine learning classification algorithms is medical diagnose.

Diagnosing if a patient has a specific disease is a simple binary classification problem.

For example, we may build a model to identify if a patient has Hepatitis C. In this case, there are two possible outputs: yes and no, which is a typical …

Continue reading Machine Learning Applications: Medical Diagnosis →