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Deep Learning with TensorFlow 2.0 Certification Training

Deep Learning with TensorFlow 2.0 Certification Training

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Edureka's Deep Learning with TensorFlow 2.0 Certification Training is curated with the help of experienced industry professionals as per the latest requirements & demands. This course will help you master popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python. You will be working on various real-time projects like Emotion and Gender Detection, Auto Image Captioning using CNN and LSTM, and many more.

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Why should you take Deep Learning with TensorFlow 2.0 Certification Training?

TensorFlow 2.0 is the most widely used Deep Learning library, developed and managed by Google

Keras is now integrated with TensorFlow 2.0 thereby making it more powerful

The average salary for "Data Scientist" is approximately $113,000 per year – Glassdoor.com

Instructor-led Deep Learning with TensorFlow 2.0 Certification Training live online classes

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Deep Learning with TensorFlow 2.0 Curriculum

Introduction to Deep Learning

Learning Objective: At the end of this module, you will be able to understand the concepts of Deep Learning and learn how it differs from machine learning. This module will also brief you out on implementing the concept of single-layer perceptron.

Topics:

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

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Getting Started with TensorFlow 2.0

Learning Objective: At the end of this module, you should be able to get yourself introduced with TensorFlow 2.x. You will install and validate TensorFlow 2.x by building a Simple Neural Network to predict handwritten digits and using Multi-Layer Perceptron to improvise the accuracy of the model.

Topics:

Introduction to TensorFlow 2.x
Installing TensorFlow 2.x
Defining Sequence model layers
Activation Function
Layer Types
Model Compilation
Model Optimizer
Model Loss Function
Model Training
Digit Classification using Simple Neural Network in TensorFlow 2.x
Improving the model
Adding Hidden Layer
Adding Dropout
Using Adam Optimizer

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Convolution Neural Network

Learning Objective: At the end of this module, you will be able to understand how and why CNN came into existence after MLP and learn about Convolutional Neural Network (CNN) by exploring the theory behind how CNN is used to predict ‘X’ or ‘O’. You will also use CNN VGG-16 using TensorFlow 2 and predict whether the given image is of a ‘cat’ or a ‘dog’ and save and load a model’s weight.

Topics:

Image Classification Example
What is Convolution
Convolutional Layer Network
Convolutional Layer
Filtering
ReLU Layer
Pooling
Data Flattening
Fully Connected Layer
Predicting a cat or a dog
Saving and Loading a Model
Face Detection using OpenCV

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Regional CNN

Learning Objective: At the end of this module, you will be able to understand the concept and working of RCNN and figure out the reason why it was developed in the first place. The module will cover various important topics like Transfer Learning, RCNN, Fast RCNN, RoI Pooling, Faster RCNN, and Mask RCNN.

Topics:

Regional-CNN
Selective Search Algorithm
Bounding Box Regression
SVM in RCNN
Pre-trained Model
Model Accuracy
Model Inference Time
Model Size Comparison
Transfer Learning
Object Detection – Evaluation
mAP
IoU
RCNN – Speed Bottleneck
Fast R-CNN
RoI Pooling
Fast R-CNN – Speed Bottleneck
Faster R-CNN
Feature Pyramid Network (FPN)
Regional Proposal Network (RPN)
Mask R-CNN

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Boltzmann Machine & Autoencoder

Learning Objective: At the end of this module, you should be able to understand what a Boltzmann Machine is and how it is implemented. You will also learn about what an Autoencoder is, what are its various types, and understand how it works.

Topics:

What is Boltzmann Machine (BM)?
Identify the issues with BM
Why did RBM come into picture?
Step by step implementation of RBM
Distribution of Boltzmann Machine
Understanding Autoencoders
Architecture of Autoencoders
Brief on types of Autoencoders
Applications of Autoencoders

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Generative Adversarial Network(GAN)

Learning Objective: At the end of this module, you should be able to understand what generative adversarial model is and how it works by implementing step by step Generative Adversarial Network.

Topics:

Which Face is Fake?
Understanding GAN
What is Generative Adversarial Network?
How does GAN work?
Step by step Generative Adversarial Network implementation
Types of GAN
Recent Advances: GAN

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Emotion and Gender Detection

Learning Objective: At the end of this module, you will be able to classify each emotion shown in the facial expression into different categories by developing a CNN model for recognizing the facial expression of the images and predict the facial expression of the uploaded image. During the project implementation, you will also be using OpenCV and Haar Cascade File to check the emotion in real-time.

Topics:

Where do we use Emotion and Gender Detection?
How does it work?
Emotion Detection architecture
Face/Emotion detection using Haar Cascade
Implementation on Colab

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Introduction RNN and GRU

Learning Objective: After completing this module, you should be able to distinguish between Feed Forward Network and Recurrent neural network (RNN) and understand how RNN works. You will also understand and learn about GRU and finally implement Sentiment Analysis using RNN and GRU.

Topics:

Issues with Feed Forward Network
Recurrent Neural Network (RNN)
Architecture of RNN
Calculation in RNN
Backpropagation and Loss calculation
Applications of RNN
Vanishing Gradient
Exploding Gradient
What is GRU?
Components of GRU
Update gate
Reset gate
Current memory content
Final memory at current time step

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LSTM

Learning Objective: After completing this module, you should be able to understand the architecture of LSTM and the importance of gates in LSTM. You will also be able to differentiate between the types of sequence based models and finally increase the efficiency of the model using BPTT.

Topics:

What is LSTM?
Structure of LSTM
Forget Gate
Input Gate
Output Gate
LSTM architecture
Types of Sequence-Based Model
Sequence Prediction
Sequence Classification
Sequence Generation
Types of LSTM
Vanilla LSTM
Stacked LSTM
CNN LSTM
Bidirectional LSTM
How to increase the efficiency of the model?
Backpropagation through time
Workflow of BPTT

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Auto Image Captioning Using CNN LSTM

Learning Objective: After completing this module, you should be able to implement Auto Image captioning using pre-trained model Inception V3 and LSTM for text processing.

Topics:

Auto Image Captioning
COCO dataset
Pre-trained model
Inception V3 model
Architecture of Inception V3
Modify last layer of pre-trained model
Freeze model
CNN for image processing
LSTM or text processing

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Deep Learning with TensorFlow 2.0 Course Description

Why Should you go for Deep Learning with TensorFlow 2.0 Certification Training?

If you are good in Python and want to start your journey towards the path of a Data Scientist then this course is definitely for you

The course is packed with the algorithms based on latest TensorFlow 2.0
Keras is now integrated with TensorFlow 2.0 thereby making it more powerful
Writing codes in TensorFlow is much more easier as compared to the previous version
TensorFlow 2.0 is now the most widely used library for Deep Learning
The course will gIve you a combined taste of text and image processing

What are the objectives of our Deep Learning with TensorFlow 2.0 Course?

After completing this course, you should be able to:

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

Who should go for this Deep Learning with TensorFlow 2.0 Course?

The Deep Learning with TensorFlow 2.0 Training is for all the professionals who are passionate about Deep Learning and want to go ahead and make their career as a Deep Learning Engineer or a Data Scientist. It is best suited for individuals who are:

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

What are the pre-requisites for this Deep Learning with TensorFlow 2.0 Course?

Required Pre-requisites:

Basic programming knowledge in Python
Concepts about Machine Learning

To help you brush up these skills, you will get the following self-paced modules as pre-requisites in your LMS:

Python for AI-ML
Statistics and Machine Learning

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Certification

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Deep Learning with Tensorflow 2.0 Project

How will I execute practicals in this Deep Learning with Tensorflow 2.0 course?

You can use google colab notebook for executing all the practical and hands-on which are a part of the module. For any doubt, the 24*7 support teams will promptly assist you.

Which case studies and demos will be a part of this Deep Learning with Tensorflow 2.0 Certification Training Course?

Classifying handwritten digits using TensorFlow 2.0
Classify the images of fashion dataset into different categories using Multiple Layer Perceptron
Classifying Dog and Cat using CNN in TensorFlow 2.0
Use CNN to categorize each face based on the facial expression
Understand the concept of Transfer Learning
Perform object detection using RCNN
Perform image denoising using the Autoencoders
Perform Emotion and gender detection using OpenCV and CNN
Use CNN and LSTM to perform Auto Image Captioning

Which projects will be a part of this Deep Learning with Tensorflow 2.0 Certification Training Course?

Domain: Healthcare Industry

Background: India has been fighting the pandemic with great spirit, with the unlocking phases being in motion the need to be proactive is now more than ever. Governments all around the world recognized the power of AI and ML in order to fight the pandemic. Since wearing a mask and avoiding crowded places is the only alternative until the vaccine is introduced, Computer vision in the form of mask detection can be a reviving factor to get life back to normal as we remember. Real-time mask detection can solve the monitoring issue in countries with large populations

Problem Statement Create a Face Mask Detector using CNN and OpenCV trained on the set of 1376 images consisting of two classes – with_mask and without_mask.

Tasks:
As a part of this project, you will be performing the following tasks:
- Prepare a detailed ipython notebook using CNN for detecting Face Masks in real-time
- Import Required Libraries
- Load and Pre-process the dataset
- Visualize the dataset
- Design a Convolutional Neural Network (CNN) Model
- Compile the Model
- Train the Model
- Evaluate the Model
- Detect the Face Masks using the HaarCascade_frontalface_default.xml file in real-time

Deep Learning with TensorFlow 2.0 Features

Instructor-led Live Sessions

30 Hours of Online Live Instructor-led Classes. Training Schedule: 10 sessions of 3 hours each.

Assignments

Each class will be followed by practical assignments.

Lifetime Access

You will get lifetime access to LMS where presentations, quizzes, installation guides & class recordings are available.

24 x 7 Expert Support

We have a lifetime 24x7 online support team to resolve all your technical queries, through a ticket based tracking system.

Certification

Successfully complete your final course project and Edureka will certify you as a Deep Learning Engineer.

Forum

We have a community forum for our learners that further facilitates learning through peer interaction and knowledge sharing.

FAQ

What if I miss a class?

"You will never miss a lecture at Edureka! You can choose either of the two options:

View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch."

What if I have queries after I complete this course?

Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.

How soon after Signing up would I get access to the Learning Content?

Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover, the access to our 24x7 support team will be granted instantly as well. You can start learning right away.

Is the course material accessible to the students even after the course training is over?

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

Will I get placement assistance?

More than 70% of Edureka Learners have reported change in job profile (promotion), work location (onsite), lateral transfers & new job offers. Edureka's certification is well recognized in the IT industry as it is a testament to the intensive and practical learning you have gone through and the real-life projects you have delivered.

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