• The course is packed with the algorithms based on latest TensorFlow 2.0
• Keras is now integrated with TensorFlow 2.0 thereby making it more powerful
• Writing codes in TensorFlow is much more easier as compared to the previous version
• TensorFlow 2.0 is now the most widely used library for Deep Learning
• The course will gIve you a combined taste of text and image processing

• 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 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

• Developers aspiring to be a 'Data Scientist'
• Analytics Managers who are leading a team of analysts
• Business Analysts who want to understand Deep Learning Techniques
• Information Architects who want to gain expertise in Predictive Analytics
• Analysts wanting to understand Data Science methodologies

• Basic programming knowledge in Python
• Concepts about Machine Learning

• Python for AI-ML
• Statistics and Machine Learning

• 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 picture?
• Step by step implementation of RBM
• Distribution of Boltzmann Machine
• Understanding Autoencoders
• Architecture of Autoencoders
• Brief on types of Autoencoders 
• Applications of Autoencoders

• 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

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• View the recorded session of the class available in your LMS.
• You can attend the missed session, in any other live batch."

Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.

Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover, the access to our 24x7 support team will be granted instantly as well. You can start learning right away.

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

More than 70% of Edureka Learners have reported change in job profile (promotion), work location (onsite), lateral transfers & new job offers. Edureka's certification is well recognized in the IT industry as it is a testament to the intensive and practical learning you have gone through and the real-life projects you have delivered.

• Once you are successfully completed your project (Reviewed by the Certhippo experts), you will be awarded with Certhippo's Selenium Training certificate.

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