Deep Learning with TensorFlow 2.0 Certification Training

Categories
AI.
Read Review
5.0 (1125 satisfied learners)

Ace Tensorflow with CertZip Deep Learning with TensorFlow 2.0 Certification Training and upskill your knowledge and technological skill.

Course Description

The Deep Learning Course with TensorFlow Certification is created according to the latest requirements & demands. This Deep learning certification course will help you grasp widespread algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python. In this Deep Learning training, you will work on real-time projects like Emotion and Gender Detection, Auto Image Captioning using CNN and LSTM, etc.

TensorFlow is an end-to-end open-source medium for machine learning. It has a broad, adjustable ecosystem of tools, libraries and community resources that helps researchers push the state-of-the-art ML and developers fast create and deploy ML-powered applications.

This certification course is for: Developers aspiring to be a 'Data Scientist.' Analytics Managers Business Analysts Information Architects Analysts wanting to understand Data Science methodologies

TensorFlow is a software tool of Deep Learning. An artificial intelligence library allows developers to create large-scale multi-layered neural networks. It is used in Classification, Recognition, Perception, Discovering, Prediction, Creation, etc. Some primary use cases are Sound Recognition, Image recognition, etc.

The prerequisite for this course is are as follow: Basic programming knowledge in Python Concepts about Machine Learning Python for AI-ML Statistics and Machine Learning

According to research, 469 companies reportedly use TensorFlow in their tech stacks, including Uber, Delivery Hero, and Hepsiburada.

TensorFlow is More Favoured in the Job Market. Community Support of TensorFlow is Uncanny. TensorFlow Offers Many Supporting Technologies. TensorFlow 2.0 is Straightforward to Operate

Tensorflow software keeps modernizing and has fast development in the years to come. It is believed to be the future of Machine Learning Modelling. Many leading institutions use it for their research aspects, like Bloomberg, google, intel, deep mind, GE health care, eBay, etc.

What you'll learn

  • In this course, you will learn: the algorithms based on TensorFlow 2.0 Keras and its integration with TensorFlow Writing codes in TensorFlow TensorFlow 2.0 text and image processing

Requirements

  • Understanding of statistics mathematics machine-learning concepts. Python

Curriculam

you will learn the concepts of Deep Learning and how it varies from machine learning. This Deep Learning Certification course will inform you on executing the idea of the single-layer perceptron.

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

you will understand TensorFlow 2. x, install and validate TensorFlow 2. x by creating an Easy Neural Network to predict handwritten digits and operating Multi-Layer Perceptron to improvise the precision of the model.

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 utilizing Easy Neural Network in TensorFlow 2. x
Improving the model
Adding Hidden Layer
Adding Dropout
Using Adam Optimizer

Candidates will discover how and why CNN came into existence after MLP and learn about Convolutional Neural Network (CNN) by learning the theory behind how CNN is used to predict 'X' or 'O', use CNN VGG-16 using TensorFlow 2 and predict whether the given image is a 'cat' or a 'dog' and save and load a model's weight.

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

Learn the concept and working of RCNN, Transfer Learning, RCNN, Fast RCNN, RoI Pooling, Faster RCNN, and Mask RCNN.

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

You will discover what a Boltzmann Machine is and how it is implemented. You will also learn what an Autoencoder is, its various types, and how it works.

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

Learn what the generative adversarial model is and how it works by implementing step by step Generative Adversarial Network.

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

classify each emotion shown in the facial expression into different categories by developing a CNN model to recognize the images' facial expressions and predict the uploaded image's facial expression. During the project implementation, you will also use OpenCV and Haar Cascade File to check the emotion in real-time.

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

You will learn 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 implement Sentiment Analysis using RNN and GRU.

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

In this section, you will learn the architecture of LSTM and the importance of gates in LSTM. You will also differentiate between the types of sequence-based models and finally increase the model's efficiency using BPTT.

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

Learn to implement Auto Image captioning using pre-trained model Inception V3 and LSTM for text processing.

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

FAQ

In developing applications for this AI engine, coders can use C++ or Python, the most popular language among deep learning researchers.

The average TensorFlow developer salary is about $148,508 per annum.

To better understand TensorFlow, one must learn as per the curriculum.

Developers are charged with designing and training neural networks using the TensorFlow framework. With the help of interactive user interfaces, TensorFlow chatbots, OCR, ICR, DataFlow graphs, and extra complicated computations, TensorFlow developers design and manage the systems and applications.

Yes, machine learning is a good career path. According to a report, Machine Learning Engineer is the top job in salary, growth of postings, and public demand.

Creating ML models involves more than just knowing ML concepts—it requires coding to do the data management, parameter tuning, and parsing results needed to test and optimize your model.

CertZip Support Unit is available 24/7 to help with your queries during and after completing Deep Learning with TensorFlow 2.0 Certification Training.

You will receive CertZip Deep Learning with TensorFlow 2.0Training certification on completing live online instructor-led classes. After completing the course module, you will receive the certificate.

By enrolling in the Deep Learning with TensorFlow 2.0Training Certification course and completing the module, you can get CertZip Natural Language Processing with Python Certification.

product-2.jpg
$370 $389
$19 Off
ADD TO CART

Training Course Features

Assessments
Assessments

Every certification training session is followed by a quiz to assess your course learning.

Mock Tests
Mock Tests

The Mock Tests Are Arranged To Help You Prepare For The Certification Examination.

Lifetime Access
Lifetime Access

A lifetime access to LMS is provided where presentations, quizzes, installation guides & class recordings are available.

24x7 Expert Support
24x7 Expert Support

A 24x7 online support team is available to resolve all your technical queries, through a ticket-based tracking system.

Forum
Forum

For our learners, we have a community forum that further facilitates learning through peer interaction and knowledge sharing.

Certification
Certification

Successfully complete your final course project and CertZip will provide you with a completion certification.

Deep Learning with TensorFlow 2.0 Certification Training

A Deep Learning with TensorFlow 2.0Training certificate is a certification that verifies that the holder has the knowledge and skills required to work with Azure technology.

Yes, Access to the course material will be available for a lifetime once you have enrolled in Edita Deep Learning with TensorFlow 2.0Training Certification Course.

demo certificate

Reviews

M Mia Martinez
T Teressa
S Summer

Related Courses

Discover your perfect program in our courses.

Edtia whatsapp-image

Contact Us

Drop us a Query

Drop us a Query

Available 24x7 for your queries