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A long time ago, when people built things on computers, they sometimes had a problem. They wanted to share their creations with others, but it was really hard to do because everyone's computer was different, like having different types of Lego blocks, or having a different version of NodeJS, Mongo or JS, having a different set of environment variables, or even working on different Operating systems.
So, a smart person named Solomon Hykes thought of an idea. He wanted to make it easy for people to put their creations in special boxes, like lunchboxes, so that these creations could work on any computer, no matter what Lego blocks (local configuration) it had.
That's how Docker started! It's like putting your favorite toys in special boxes so you can take them to your friend's house and play with them there. These special boxes are called "containers."
# This is a Dockerfile! It's like a recipe to make a special box for our computer toys.
# First, we need a special box to start with. We'll use a box that already has some things inside it.
FROM magic_box
# Now, we want to put our favorite toy car inside the special box.
ADD toy_car /toys/
# We also want to add some yummy snacks, like cookies, to our box.
ADD cookies /snacks/
# We can even write down a note to remind ourselves to share this box with our friends.
LABEL note="Please share with friends!"
# Finally, we'll close the box and seal it up. Now, our special box is ready!
Docker makes it super simple for people to share their computer creations with others. It's like sharing your toys with friends without worrying about them getting mixed up or broken.
So, Docker is like a magical way to keep things neat and tidy when sharing stuff between computers. It's like having special lunchboxes for your computer games and making them easy to share with friends.
Cool, right?
- Machine learning is a type of artificial intelligence that learns from data, whereas deep learning, a subset of machine learning, sorts data in layers for comprehensive analysis. - AI is technology that mimics human cognition, machine learning lets computer models learn from a data set, and deep learning uses neural networks to learn from large amounts of data. - Convolutional Neural Networks (CNNs) are crucial in both machine learning and deep learning. They enable image recognition in machine learning and help deep learning algorithms understand complex features in data. - Machine learning offers quick learning from limited data, like Spotify's music recommendations. Deep learning, utilized in complex tasks like self-driving cars, uses artificial neural networks to analyze large data sets. - The future of machine learning and deep learning is promising, with machine learning predicted to become more superior in deciphering complex data patterns and deep learning providing possibilities for processing large volumes of unstructured data.
- Kotlin and Java are both used in Android development, but Kotlin has been announced as Google's preferred language for Android apps in 2017. - Kotlin offers improved efficiency, modern solutions to Java's shortcomings, and interoperability with existing Java codes. - While Java and Kotlin's raw performance is similar, Kotlin's modern features, like inline functions, can boost execution performance. - Kotlin has more concise syntax, enhanced safety features, and better scalability than Java, despite consuming slightly more memory. - Java is reliable and has a robust support community but is more verbose and not null-safe like Kotlin. - Kotlin developers generally earn higher salaries reflecting expertise in a newer language, but Java developers are highly demanded due to the universality of Java. - The comparison of Kotlin and Java is also relevant in backend development, with Kotlin providing concise and readable codes, and Java offering time-tested stability. - The developer community holds a wide range of views on Kotlin and Java, with a noticeable shift towards Kotlin in Android development after Google's endorsement. However, Java remains a critical language for certain projects. - Kotlin may be easier for beginners due to its clean syntax and modern features that allow avoiding common pitfalls like null pointer exceptions. - Ultimately, the choice between Kotlin and Java should depend on project requirements, personal preferences, and the skill set of the development team.
- The concept of artificial intelligence (AI) goes back to ancient myths and the idea of creating automatons. - AI implies the capacity of a machine to mimic human behavior. - The AI era began in the mid-twentieth century with thinkers such as Alan Turing. - Key milestones include the introduction of the Turing Test (1950), and the coining of the term 'artificial intelligence' at the Dartmouth Workshop (1956). - Significant developments in the 1950s and 1960s include machine learning, natural language processing, and creation of the first AI robot. Key contributors were John McCarthy and Marvin Minsky. - The 1980s and 1990s saw AI go mainstream with developments in machine learning and the rise of the internet. AI began influencing various fields. - The early 2000s brought home-centric AI like Roomba and virtual assistants like Siri. By the 2010s, AI revolutionized sectors like healthcare, finance, and web services. - Notable figures in the 21st-century AI advancement include Elon Musk, Stuart Russell, and Peter Norvig. - Today, AI is a part of daily life from mobile phones to home appliances. Future predictions include AI teaching itself, creating more AI, predicting diseases, and reducing energy use.
