Deep Learning with TensorFlow 2.0 Certification Training in Milan, Italy

  • 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

Deep Learning Training Options in Milan, Italy

Online Self-Learning

Online self-learning courses are currently not available.

Please email support@encertify.com to enquire for more information.

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

Deep Learning with TensorFlow 2.0 Certification & Exam

Towards the end of the course, you will work on a project involving Deep Learning with TensorFlow 2.0. On successful completion of the project, you will be certified as a "Deep Learning Engineer".

Why choose our Deep Learning in Milan, Italy?

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More than 12000 satisfied learners have taken this course on Deep Learning.

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 throughĀ ourĀ programs

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