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Deep Learning with TensorFlow 2.0 Certification Training in Frankfort
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Learn and get certified in Deep Learning with TensorFlow 2.0 from the comfort of your home and transform your career
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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
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30 hours of Instructor-led Deep Learning with TensorFlow 2.0 Certification Classes with Hands-on practice with Google Colab notebook
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We guarantee great value at the lowest price in the industry
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Exclusive Buy 1 - Get 1 Free Offer (till 31st May, 2021) Know More
Online Self-Learning
USD 299 179
40
Live-Virtual Class
Date :
Jun 04 Jul 03 (Weekend)
Details
Time : 08:30 PM to 11:30 PM (CDT)
USD 599 369
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Date :
Jul 03 Aug 01 (Weekend)
Details
Time : 10:00 AM to 01:00 PM (CDT)
USD 599 369
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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".
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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.
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All our trainers are highly qualified and certified in various industry frameworks. They have years of professional experience in their respective fields and they are not only experts in their domains but are also passionate about sharing their knowledge and expertise with other professionals thereby enriching careers of students.
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In case you miss a session because of any reason, you can either attend the missed session in any other live batch or view the recorded session in the LMS.
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You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.
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Deep Learning with TensorFlow 2.0 Frequently Asked Questions
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