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Deep Neural Networks, Convolutional Neural Networks, Object Detection, Computer Vision, LSTM, Tensor Flow Certification

What you will learn

Neural Network Basics, Multi Layered Perceptron, Convolutional Neural Networks

Object Detection, Computer Vision

Practical applications of Deep Neural Networks, Real world case studies

Tensor flow Beginner to Professional and Tensor flow Certification

Description

This course not only simplifies complex theoretical Deep Learning concepts but also teaches to solve real world problems using Deep Neural Networks. There are sufficient number of Real world Projects discussed in this course in order to make learner Job ready. The important aspect of this course is to prepare learner for Google Tensor Flow Certification Examination. Apart from Deep Neural fundamentals here we discuss Convolutional Neural Networks, Long-short term memory(LSTM), Generative Adversarial Networks (GANs), Encoder Decoder Models, Attention Models, Image Segmentation. This course also teaches Google Tensor Flow from a beginners stand point. One of the main aim of this course is to make learner a professional in Tensor Flow. Case studies like Self Driving Car have been discussed in great detail. After taking this course the learner will be expert in following topics.

a) Theoretical Deep Learning Concepts.

b) Convolutional Neural Networks

c) Long-short term memory

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d) Generative Adversarial Networks

e) Encoder- Decoder Models

f) Attention Models

g) Object detection

h) Image Segmentation


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i) Transfer Learning

j) Open CV using Python

k) Building and deploying Deep Neural Networks

l) Professional Google Tensor Flow developer

m) Using Google Colab for writing Deep Learning code

n) Python programming for Deep Neural Networks

The Learners are advised to practice the Tensor Flow code as they watch the videos on Programming from this course.

English
language

Content

Neural Networks and Deep Multi Layered Perceptron
Introduction to Biological Neuron
Lecture 2: Logistic Regression and Neuron
Lecture 3: Multi Layered Perceptron
Lecture 4: DNN Notations
Lecture 5: Training a single Neuron model
Lecture 6: Training a Multi Layered Perceptron
Lecture 7: Memoization