Introduction to DeepJavaLibrary

Introduction to DeepJavaLibrary: An open-source, engine-agnostic, Java library for training and inference deep neural networks

Java has been one of the most popular programming languages in enterprise for a long time and has a massive ecosystem of libraries, and frameworks, and a large community of developers. However, there are very limited options offered in Java for deep learning applications. Currently, most deep learning models are written and trained in Python. This presents an additional barrier to entry for Java developers who want to enter this area, as they have to learn both a new programming language and the complex domain of deep learning.

In order to lower the barrier of entry into deep learning for Java developers, AWS built Deep Java Library (DJL) , an open source deep learning framework in Java to bridge the gap for Java developers by supporting any deep learning engine, such as Apache MXNet, PyTorch, or TensorFlow to run training and inference natively in Java. It also contains a powerful ModelZoo design that allows you to manage trained models and load them in a single line of code. The built-in ModelZoo currently supports more than 70 pre-trained and ready to use models from GluonCV, HuggingFace, TorchHub and Keras. If you’ve been a Java developer and are interested in exploring deep learning, Deep Java Library (DJL) is a great place to start.

In this tutorial, we walk through an example demonstrating the training capabilities of DJL by training a simple model on the popular MNIST dataset.

What is Deep Learning?

Machine learning is the process of letting the computer learn the specifications of the given task from data through the use of various statistical techniques. This ability to learn the features of a task allows computers to perform complex tasks, such as detecting an object in an image, that were generally considered to be beyond the scope of computers because of the difficulty of providing exact specifications for every possible case.

Deep learning is a branch of machine learning based on artificial neural networks. An artificial neural network is a programming paradigm inspired by the human brain that helps the computer learn and perform tasks based on observational data. Deep learning is a collection of powerful techniques that can be leveraged to help train large artificial neural networks to perform complex tasks. Deep learning techniques have proven to be very effective at solving complex tasks like object detection, action recognition, machine translation, natural language understanding among others.

Train MNIST with DJL

Setting up the project

You can use the following configuration in your gradle project to import the required dependencies. In this example, we use the api package which contains the core API of the DJL project, and the basicdataset package which contains some of the basic datasets in DJL. Since we are training with the MXNet engine, we are also going to import the mxnet-engine package, and the mxnet-native-auto package.

plugins {
    id 'java'
}
repositories {                           
    jcenter()
}
dependencies {
    implementation "ai.djl:api:0.8.0"
    implementation "ai.djl:basicdataset:0.8.0"
    // MXNet
    runtimeOnly "ai.djl.mxnet:mxnet-engine:0.8.0"
    runtimeOnly "ai.djl.mxnet:mxnet-native-auto:1.7.0-backport"
}

NDArray and NDManager

NDArray is the core data structure for all mathematical computations in DJL. An NDArray represents a multidimensional, fixed-size homogeneous array. NDArray behaves similar to the python program numpy.

NDManager is the manager of NDArray. NDManager manages the lifecycle of NDArray, and is an essential part of memory management in DJL. Every NDArray created by an instance of NDManager will be closed once that NDManager is closed. NDManager, and NDArray both extend AutoCloseable. To know and understand the usage of NDArray and NDManager better, see this blog post.

Model

In DJL, training and inference start with a Model. In this article, we concentrate on the training process. To start the training process we create a new instance of the Model class. The Model class also extends AutoCloseable. So, it is created with a try-with-resources.

try (Model model = Model.newInstance()) {
    ...
    // training process takes place here
    ...
}

Preparing the data

The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems. The MNIST dataset is readily available in DJL. The shape of an individual image from the MNIST dataset in DJL is (28, 28). If you wish to train a model on your own dataset, you can do that by adding your own dataset by following the instructions here.

A random collection of images from the MNIST dataset, Image by Keerthan Vasist

In order to train your model, you first need to load the dataset.

int batchSize = 32;
Mnist trainingDataset = Mnist.builder()
        .optUsage(Usage.TRAIN)
        .setSampling(batchSize, true)
        .build();
Mnist validationDataset = Mnist.builder()
        .optUsage(Usage.TEST)
        .setSampling(batchSize, true)
        .build();

This code creates training and validation datasets. The dataset is also configured to sample the dataset randomly. There are further configurations made on the dataset like applying transforms on the images, or limiting the size of the dataset.

#java #deep-learning #machine-learning #developer

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Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

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Introduction to VMware

What is VMware?
VMware is a virtualization and cloud computing software provider. Founded in 1998, VMware is a subsidiary of Dell Technologies.

VMware Certification Levels

• VMware Certified Technical Associate
• VMware Certified Professional
• VMware Certified Advanced Professional
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VMware certification salary?

• VCP: VMware Certified Professional: $88,000
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• VCDX: VMware Certified Design Expert: $156,000
• VCA-DT: VMware Certified Associate: $81,000
• VCP-DT: VMware Certified Professional: $98,000
• VCAP-DT: VMware Certified Advanced Professional: $115,000

Top Companies which are using VMware

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• Allied Digital Services Ltd
• Mondelez International, Inc.

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Introduction to Chatbots - How Chatbots work? 1/4

Many companies today claim that they have chatbots running on NLP and that they are creating responses on the fly. But how do Chatbots work?

Chatbots are aiming to make natural interactions indistinguishable from human conversations, but how much is true? What goes into such a backend?

Let’s discuss.

Table of Contents[show]

How do Chatbots work?

Chatbots work using multiple methodologies. A few of those techniques are easy ones, based on keyword extraction. Some of the techniques respond with FAQ sections.

While some are more advanced ones like Haptik that work on NLP and respond in a much more human-like manner. Let’s find out the different ways of how chatbots work. In the upcoming articles, we’ll talk about creating your first chatbot.

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A Swift Introduction To Probability Theory With Python

An overview of programming foundational calculus and statistics behind probability theory in Python.

Introduction

Probability theory is a sect of mathematics that focuses on the interpretations of the likelihood of certain outcomes. The mathematical branch uses axioms to formally measure probability in probable space. Probable space is a name given to sample space that is used to determine likelihood. Given the definition, it is easy to see why probability theory takes a strong hold in experiments and predicting the outcomes of those experiments. Using data and probable space, we can make informed decisions using data based on prior results. Just as well, we can determine analytical correlations that can provide valuable information about the world we live in.

Needless to say, this makes this branch of mathematics pretty powerful. Not only can we use these mathematics to make informed decisions about the world around us and how to improve it, but we can also use it to predict the impact of those decisions. This in turn makes us even more informed about the potential outcome of the decisions made from these analytics. Now that we understand the power of probability theory, let’s dive into probable space, distributions, and how we can use them in the Python programming language.

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Introduction to OpenCV

OpenCV is a python library, that is very useful in Visualization and Analyzing purposes.

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