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Course Overview

Courses Overview

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About Course

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

COURSE SYLLABUS

  • Machine Learning Primer
  • Machine Learning core concepts, scalable algorithms, project workflow.
  • Objective Functions and Regularization
  • Understanding Objective Function of ML Algorithms
  • Metrics, Evaluation Methods and Optimizers
  • Popular Metrics in Detail: R2 Score, RMSE, Cross Entropy, Precision, Recall, F1 Score, ROC-AUC, SGD, ADAM
  • Artificial Neural Network
  • ANN in detail, Forward Pass and Back Propagation
  • Machine Learning Vs Deep Learning
  • Core difference b/w ML and DL from implementation perspective

 

  • Python Programming Primer
  • Installing Python, Programming Basics, Native Data types
  • Class, Inheritance and Magic Functions
  •  Python Classes, Inheritance Concepts, Magic Functions
  • Special Functions in Python
  • Overview, Array, selecting data, Slicing, Iterating, Array Manipulations, Stacking, Splitting arrays, Key functions
  •  Decorators and Special Functions
  • Decorators implementation with class
  • Context Manager ‘with’ in Python
  • Context Manager Application
  • Exception Handling
  • Try and Catch block
  • Python Package Management
  • Bundling and export python packages
  • TensorFlow 2.0 Basics
  • TensorFlow core concepts, Tensors, core APIs
    Copyright © IABAC 2017-2020. IABAC®, IABAC logo®, DSF®, CDS®, CAIE®, CNLPE®, CCVE®, CDSHR®, CDSFIN®, CVAE®, CDLE®, CDSHC®, CDSMKT®, CDS®, DSCMGR®, AICE®, CDSD® and CMLE® are registered trademarks of IABAC BV, The Netherlands. Registration Number (KVK-nummer) :73234893. Reproduction of any part of this material requires written permission of IABAC BV. All rights reserved. EN_CDLE_2020_Syllabus_V2.0
  • Concrete Functions, Datatypes, Control Statements
  • Polymorphic Functions, Concrete Functions, Datatypes, Control Statements, NumPy, Pandas

  • Autograph eager execution

  • tf.function autograph implementation

  • Sessions vs tf.function

  • Keras (TensorFlow 2.0 Built-in API) Overview

  •  Sequential Models, configuring layers, loading data, train and test, complex models, call backs, save and restore Neural Network weights

  • Building Neural Networks in Keras

  • Building Neural networks from scratch in Keras

  • Implementing RNN, CNN in Keras

  • Building Recurrent Neural Networks for sequence data and Convolution Neural Networks for Image Classification

 

  • Linear Algebra
  • Vectors, Matrices, Linear Transformation, Eigen Vectors, Matrix Operations, Special Matrices
  • Calculus – Derivatives: Calculus essentials, Derivatives and Partial Derivatives, Chain Rule, Derivatives of special functions
  • Probability Essentials: Probability basics and notations, Conditional probability, Essential Probability theorems for Machine Learning
  • Special functions: Relu, Sigmoid, SoftMax, Popular Loss Functions – Cross Entropy, Quadratic Loss Functions
  • Deep Learning Network Concepts
  • Core concepts of Deep Learning Networks
  •  Deep Dive into Activation Functions
  • Relu, Sigmoid, Tanh, SoftMax, Linear
  • Building simple Deep Learning Network
  • Simple DL network from starch
  • Tuning Deep Learning Network
  •  Tuning Deep Learning Network Parameters for optimal performance, Stopping Criteria
  •  Visualizing Training using TensorBoard
  •  Visualizing Deep Learning Network using TensorBoard
  • Deep Learning Architectures
  • Popular Deep learning Architectures – CNN, RNN, LSTM RNN, GRU RNN Introduction
  • Deep Dive into Convolutional Neural Network
  • Core Concepts of Convolutional Neural Network, Feature Maps, Relu Activation, Max Pooling
  • CNN Application – Image Classification
  • Image Classification implementation with CNN TensorFlow 2.0 (Keras)
  • Recurrent Neural Networks (RNN) Basics

Course Content

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