High Quality Deep Learning with TensorFlow 2.0 Certification Training in Hartford, CT

Deep Learning with TensorFlow 2.0 Certification Training in Hartford, CT

Learn and get certified in Deep Learning with TensorFlow 2.0 from the comfort of your home and transform your career
Curated by industry experts, the course will help you master popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python
Deep Learning with TensorFlow 2.0 Certification training with Hands-on practice with Google Colab notebook
We guarantee great value at the lowest price in the industry

Deep Learning with TensorFlow 2.0 Overview

Encertify's Deep Learning with TensorFlow 2.0 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 also be offered the opportunity to work on various real-time projects like Emotion and Gender Detection, Auto Image Captioning using CNN and LSTM, and many more.

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 the 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

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Deep Learning with TensorFlow 2.0 Course 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

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

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

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

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

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

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

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

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

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

Why choose our Deep Learning in Hartford, CT?

Low Cost

Without compromising on quality, we have priced our Deep Learning with TensorFlow 2.0. certification courses very competitively. We guarantee that you will find us more economical than any other training provider.

Unmatched Quality

We along with our affiliate partners are dedicated in creating the best quality study materials and student experience across our products. All content complies with quality conformance standards to ensure that our content is the best and free of any errors.

Course Design

Based on years of experience in delivering effective professional training, our courses are designed not only to provide you the Deep Learning certification, but also to empower with best practices.

We achieve this by providing a unique blend of concepts, case studies, and simulations that guarantee our students know how to implement Deep Learning concepts in real life.

Lifetime Access

You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.

Customer Satisfaction

We’re here 24/7 to ensure you’re heard and supported, no matter what questions or doubts you may have. Our team is committed to delivering exceptional customer service to every individual and organization we serve.

Deep Learning with TensorFlow 2.0 Frequently Asked Questions

Yes, we do offer additional discounts to group and corporate training customers. Please email us at support@encertify.com to find out more about our group discount offerings.

The orientation class/session covers the overall course curriculum and any installation (if applicable). This will prepare you for the subsequent program, which will start next week.

Use the "Submit your query"section in this page or check "Contact Us" section. Alternatively, please send an email to support@encertify.com to find out more about our course offerings.

If you're unemployed right now, or you're a student taking this course for career growth, we do provide additional discounts for you on selected courses. Please email support@encertify.com to avail this benefit and discount coupon.

Note: These discounts are available on selected courses only.

We do not offer placement or placement assistance services at this time. However, our training is designed to equip you with in-demand skills, hands-on experience, and certification readiness to help you confidently pursue new career opportunities. Many of our learners have successfully transitioned into new roles or advanced in their careers based on the knowledge and certifications gained through our programs

Deep Learning with TensorFlow 2.0 Certification Training in Hartford, CT

Hartford, Connecticut – Bridge Insurance, Finance & Tech with Certification

Hartford is a traditional hub of insurance, finance, and related professional services, often called the “Insurance Capital” of the world. In recent years, these sectors have increasingly integrated digital operations, analytics, and system modernization.

Training and certification in Data Science & BI, AI, Cloud & DevOps, Project Management, Agile & Scrum, and Quality Management help you access roles in insurance analytics, fintech innovation, risk modeling, process optimization, and digital transformation for financial firms. The overlap of regulated environments and data sensitivity means that firms highly value professionals who can manage operations reliably.

If you invest in certification, you position yourself as someone who can bridge traditional finance/insurance with emerging technology-driven practices. In Hartford, your skills are relevant, demanded, and rewarded in a region balancing legacy strength with ongoing modernization.

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