Online Course: AI & Deep Learning With Python

Online Course: AI & Deep Learning With Python

This is a specialization course which will help you to get a break into AI and Deep Learning with a complete project.

course at a glance

  • Date : 4 Feb - 22 Jul 2022
  • No. of Classes/ Sessions : 25
  • Total Hours : 75
  • Last Date of Registration : 3 Feb 2022
  • Class Schedule :
    • Friday - 3:00 PM - 6:00 PM
  • venue : Online Training Platform

Price: TK. 12,000
(Excluding VAT & TAX)

COURSE OUTLINE

1

  INTRODUCTION TO DEEP LEARNING

  • What are the Limitations of Machine Learning?
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning
  • Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning

 2

  INTRODUCTION TO ARTIFICIAL INTELLIGENCE (AI)

  • History of AI
  • Modern era of AI
  • How is this era of AI different?
  • Transformative Changes
  • Role of Machine learning & Deep Learning in AI
  • Hardware for AI (CPU vs. GPU vs. TPU)
  • Software Frameworks for AI
  • Deep Learning Frameworks for AI
  • Key Industry applications of AI

  DEEP LEARNING IN PYTHON

  • Overview of important python packages for Deep Learning

 4

  OVERVIEW OF TENSOR FLOW

  • What is Tensor Flow?
  • Tensor Flow code-basics
  • Graph Visualization
  • Constants, Placeholders, Variables
  • Tensorflow Basic Operations
  • Linear Regression with Tensor Flow
  • Logistic Regression with Tensor Flow
  • K Nearest Neighbor algorithm with Tensor Flow
  • K-Means classifier with Tensor Flow
  • Random Forest classifier with Tensor Flow

 5  NEURAL NETWORKS USING TENSOR FLOW

  • Quick recap of Neural Networks
  • Activation Functions, hidden layers, hidden units
  • Illustrate & Training a Perceptron
  • Important Parameters of Perceptron
  • Understand limitations of A Single Layer Perceptron
  • Illustrate Multi-Layer Perceptron
  • Back-propagation – Learning Algorithm
  • Understand Back-propagation – Using Neural Network Example
  • TensorBoard

DEEP LEARNING NETWORKS

  • What is Deep Learning Networks?
  • Why Deep Learning Networks?
  • How Deep Learning Works?
  • Feature Extraction
  • Working of Deep Network
  • Training using Backpropagation
  • Variants of Gradient Descent
  • Types of Deep Networks
  • Feed forward neural networks (FNN)
  • Convolutional neural networks (CNN)
  • Recurrent Neural networks (RNN)
  • Generative Adversal Neural Networks (GAN)
  • Restrict Boltzman Machine (RBM)

 7

  CONVOLUTIONAL NEURAL NETWORKS (CNN)

  • Introduction to Convolutional Neural Networks
  • CNN Applications
  • Architecture of a Convolutional Neural Network
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • Transfer Learning and Fine-tuning Convolutional Neural Networks

 

8 RECURRENT NEURAL NETWORKS (RNN)

  • Intro to RNN Model
  • Application use cases of RNN
  • Modelling sequences
  • Training RNNs with Backpropagation
  • Long Short-Term Memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model

 9

  RESTRICTED BOLTZMANN MACHINE (RBM)

  • What is Restricted Boltzmann Machine?
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders & Applications
  • Understanding Autoencoders

 10

  DEEP LEARNING WITH TFLEARN

  • Define TFlearn
  • Composing Models in TFlearn
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with TFlearn
  • Customizing the Training Process
  • Using TensorBoard with TFlearn
  • Use-Case Implementation with TFlearn

 11

  DEEP LEARNING WITH KERAS

  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with Keras
  • Customizing the Training Process
  • Using TensorBoard with Keras
  • Use-Case Implementation with Keras
  • Intuitively building networks with Keras

FINAL PROJECTS- CONSOLIDATE THE LEARNING & IMPLEMENT THEM IN PYTHON

  • Computer Vision
  • Text Data Processing
  • Image processing - PNG, PDF,JPEG, JPG etc.
  • Speech analytics - Speech to text / Voice tonality

Curriculum

Training On AI & Deep Learning With Python 75 Hrs

Tentative Class Start

4th February, 2022

Available Seat

10 / 25

who can join


Anyone Having Primary Knowledge of Machine Learning.

Analytics professionals or aspirants with prior working knowledge of Data Science with Python, who are looking Deep Learning certification to up-skill with practical application of AI Deep Learning with TensorFlow and Keras.

 

Meet the Instructor