DEEP LEARNING

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deep

OBJECTIVE 

Our Objective of this course is to give basic understanding of neural networks and their applications  in computer vision and natural language understanding. The course starts with linear models and deep  neural networks. This Course also deals with all popular building blocks of neural networks including  fully connected layers, convolutional and recurrent layers including complex modern architectures in  TensorFlow and Keras frameworks. 

EXIT PROFILE 

Complete Knowledge in Deep Learning. 

Programming, Probability and Statistics. 

Data Modeling. 

Machine Learning Algorithms & System Design. 

CAREER PATH 

Data Analysist 

Data Scientist 

Machine Learning Engineer 

NLP Scientist 

FACULTY SKILL SET 

Knowledge in Data science 

Knowledge in R programming 

Knowledge in Programming, Probability and Statistics 

Knowledge in Machine Learning 

HARDWARE AND SOFTWARE REQUIREMENTS 

Operating System: Windows 7/8/10  

Minimum Memory: 1 GB  

Recommended Memory: 4 GB / 8 GB  

Minimum Disk Space: 160 GB  

Recommended Disk Space: 1 TB 

PREREQUISITE FOR STUDENTS  

Before attending this course, students must have:  

Knowledge in programming will be an added advantage. 

Basic Knowledge in Programming, Probability and Statistics 

COURSE OUTLINE 

Introduction to deep learning 

Neural Network Basics 

Perceptron Learning Algorithm 

Feedforward neural network 

Training Neural Network

Conditional Random Fields 

Deep Learning 

Probabilistic Neural Network 

Deep Learning research 

Deep Learning Tools

Course Features

  • Lectures 63
  • Quizzes 0
  • Duration 72 hours
  • Skill level All levels
  • Language English
  • Students 0
  • Assessments Yes

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