Instructor-led Deep Learning with TensorFlow 2.0 Certification Training live online classes
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Jun 05th | SAT & SUN (5 WEEKS) Weekend Batch | ⚡FILLING FAST Timings – 07:00 AM to 10:00 AM (IST) |
Adda For Your Certification Needs
CertAdda’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.
$432.00 $399.00
Date |
Duration |
Timings |
---|---|---|
Jun 05th | SAT & SUN (5 WEEKS) Weekend Batch | ⚡FILLING FAST Timings – 07:00 AM to 10:00 AM (IST) |
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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To help you brush up these skills, you will get the following self-paced modules as pre-requisites in your LMS: