PRODUCT DESCRIPTION

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.

OBJECTIVES

  • 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

ADVANTAGES

  • 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