you will learn the concepts of Deep Learning and how it varies from machine learning. This Deep Learning Certification course will inform you on executing the idea of the single-layer perceptron.

you will understand TensorFlow 2. x, install and validate TensorFlow 2. x by creating an Easy Neural Network to predict handwritten digits and operating Multi-Layer Perceptron to improvise the precision of the model.

Candidates will discover how and why CNN came into existence after MLP and learn about Convolutional Neural Network (CNN) by learning the theory behind how CNN is used to predict 'X' or 'O', use CNN VGG-16 using TensorFlow 2 and predict whether the given image is a 'cat' or a 'dog' and save and load a model's weight.

Learn the concept and working of RCNN, Transfer Learning, RCNN, Fast RCNN, RoI Pooling, Faster RCNN, and Mask RCNN.

You will discover what a Boltzmann Machine is and how it is implemented. You will also learn what an Autoencoder is, its various types, and how it works.

Learn what the generative adversarial model is and how it works by implementing step by step Generative Adversarial Network.

classify each emotion shown in the facial expression into different categories by developing a CNN model to recognize the images' facial expressions and predict the uploaded image's facial expression. During the project implementation, you will also use OpenCV and Haar Cascade File to check the emotion in real-time.

You will learn 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 implement Sentiment Analysis using RNN and GRU.

In this section, you will learn the architecture of LSTM and the importance of gates in LSTM. You will also differentiate between the types of sequence-based models and finally increase the model's efficiency using BPTT.

Learn to implement Auto Image captioning using pre-trained model Inception V3 and LSTM for text processing.