Java Deep Learning Training generally involves diving into Data Science future and also learning the process of building sophisticated algorithms that are basic to deep learning with AI or Java.


  • Practical implementation of deep dive into machine learning and Deep Learning Algorithms
  • Implementation of ML Algorithms having relation to deep learning
  • Understanding deep learning library DL4J and its practical use


  • Applicable to Machine Learning to Fraud, anomaly and outlier detection
  • Selection and splitting data set into training, test and validation
  • Completely secure for deployment of Blockchain Application using Java
Duration: 2 Days

Course Content:
  1. Basic Overview of Java Deep Learning

    • Understanding Transition of AI
    • Things differentiating Machine and Human
    • Using AI and Deep Learning
  2. Machine Learning Algorithms

    • Basic need for Training in machine learning
    • Using Supervised and unsupervised learning
    • Understanding Machine Learning Application Flow
    • Theories and Algorithms for Neural Networks
  3. Concept of Deep Belief Nets and Stacked Denoising Autoencoders

    • Basic Neural Network Fall
    • Understanding revenge of Neural Network
    • Basics of Deep Learning Algorithms
  4. Using Dropout and Convolutional Neural Networks

    • DL Algorithms without pre-training
    • Understanding Dropout
    • Understanding Convolutional Neural Networks
  5. Exploration of Java Deep Learning Libraries

    • Introduction to DL4J and ND4J
    • Implementation with ND4J
    • Implementation with DL4J
  6. Practical Implementation for RNN

    • Difficulties for DL
    • Approaches for maximizing DL Possibilities and abilities
  7. More DL Libraries

    • Theano
    • TensorFlow
    • Caffe
  1. Applied Machine Learning Quickstart

    • Understanding ML and Data Science
    • Using Data and Problem Definition
    • Collecting Data concepts
    • Pre-processing of Data
    • Concept of Supervised and Unsupervised Learning
    • Overview of Generalization and Evaluation
  2. Basics of Java Libraries and Platforms for Machine Learning

    • Understanding ML Libraries

      • Weka
      • Java ML
      • Apache Mahout
      • Apache Spark
      • Deeplearning4j
      • MALLET
    • ML Application Building Concept
  3. Overview of basic Algorithms for Classification, Regression and Clustering

    • Classification Algorithms
    • Regression Algorithms
    • Clustering Algorithms
  4. Concept of Customer Relationship Prediction with Ensembles

    • Customer relationship Database
    • Overview of Naive Bayes Classifier baseline
    • Overview of Modelling
    • Concept of Advanced Modelling with ensembles
  5. Understanding the concept of Affinity Analysis

    • Market Basket Analysis
    • Using Association Rule Learning
    • Working with supermarket dataset
    • Understanding Discover Patterns
  6. Engine Recommendation using Apache Mahout

    • Basic Concepts
    • Fetching Apache Mahout
    • Concept of building recommendation Engine
  7. Dealing with Fraud and Anomaly Detection

    • Suspicious Pattern Detection
    • Unsuspicious Pattern Detection
    • Basic Insurance Claims for Fraud Detection
    • Website Traffic for Anomaly Detection