Deep-Learning

Deep Learning Training with Tensorflow

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Last Update April 12, 2022
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About This Course

Deep learning is fast becoming among the most popular trends to be embraced by high profile companies. Deep Learning has made business more viable across healthcare, genomics, cybersecurity, e-commerce, agriculture and other sectors.

Deep Learning Training with Keras and TensorFlow Certification by Ninjaz Academy is curated with the help of experienced industry professionals as per the latest requirements & demands. This Deep learning certification course will help you master popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow package in Python. In this Deep Learning training, you will be working on various real-time projects like Emotion and Gender Detection, Auto Image Captioning using CNN and LSTM, and many more.

This Deep Learning course with Keras and TensorFlow certification training is developed by industry leaders and aligned with the latest best practices. You’ll master deep learning concepts and models using TensorFlow frameworks and implement deep learning algorithms, preparing you for a career as Deep Learning Engineer.

In this Deep Learning course with TensorFlow certification training, you will become familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and more. Upon completion, you will be able to build deep learning models, interpret results, and build your own deep learning project.

Key Features:

  • Flexible Schedule
  • Tailor Made Training
  • 24 x 7 Expert Support
  • Access to the Recorded Sessions
  • Course material prepared by SMEs
  • Get certified at an affordable price
  • 30 Hours Instructor-led Online Training
  • Get key resources from Ninjaz Academy
  • Expert Deep Learning Certified instructors across the globe
  • Deep Learning sample papers to prepare for your certification exam

Learning Objectives

Get yourself introduced and trained with TensorFlow 2.0.
Understand the concept of Single Layer and Multi Layer Perceptron by implementing them in Tensorflow 2.0
Learn about the working of CNN algorithm and classify the image using the trained model
Grasp the concepts on important topics like Transfer Learning, RCNN, Fast RCNN, RoI Pooling, Faster RCNN, and Mask RCNN
Understand the concept of Boltzmann machine and Auto Encoders
Implement Generative Adversarial Network in TensorFlow 2.0
Work on Emotion and Gender Detection project and strengthen your skill on OpenCV and CNN
Understand the concept of RNN, GRU, and LSTM
Perform Auto-Image Captioning using CNN and LSTM

Requirements

  • We recommend applicants to have knowledge of programming (preferably in Python)
  • Familiarity with statistics, algebra, probability and exposure to data analysis is preferred.

Target Audience

  • Developers aspiring to be a 'Data Scientist'
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Deep Learning Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • Analysts wanting to understand Data Science methodologies

Curriculum

120 Lessons

Introduction to Deep Learning

What is Deep Learning?
Curse of Dimensionality
Machine Learning vs. Deep Learning
Use cases of Deep Learning
Human Brain vs. Neural Network
What is Perceptron?
Learning Rate
Epoch
Batch Size
Activation Function
Single Layer Perceptron

Getting Started with TensorFlow 2.0

Convolution Neural Network

Regional CNN

Boltzmann Machine & Autoencoder

Generative Adversarial Network(GAN)

Emotion and Gender Detection

Introduction RNN and GRU

LSTM

Auto Image Captioning Using CNN LSTM

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