Udemy

Deeplearning :Convolutional Neural Networks in Python

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  • 08 Students
  • Updated 2/2020
3.0
(01 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
2 Hour(s) 3 Minute(s)
Language
English
Taught by
Arpan Gupta
Rating
3.0
(01 Ratings)

Course Overview

Deeplearning :Convolutional Neural Networks in Python

CNN with Python

  • Anyone interested in Deep Learning

  • Students who have at least high school knowledge in math and who want to start learning Deep Learning

  • Any intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning

  • Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets

  • Any students in college who want to start a career in Data Science

  • Any data analysts who want to level up in Deep Learning

  • Any people who are not satisfied with their job and who want to become a Data Scientist

  • Any people who want to create added value to their business by using powerful Deep Learning tools

  • Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business

  • Any Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithms


Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.

Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks and Transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance

Course Content

  • 3 section(s)
  • 9 lecture(s)
  • Section 1 Introduction
  • Section 2 What are Convolutional Neural Networks
  • Section 3 Implementation of deeplearning algo in Keras and Tensorflow

What You’ll Learn

  • Convolutional Neural Networks for computer vision AI problems


Reviews

  • U
    Ujwala Patil
    3.0

    make it more technical

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