Deep Learning with TensorFlow 2.0 Certification Training

Learning objective: In this module, you will study the concepts of Deep learning and learn how it differs from 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

Learning objective: In this module, you will study TensorFlow 2.0, its installation procedures, and build a simple neural network to predict handwritten digits.

• 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 employing the Simple Neural Network in TensorFlow 2.x
• Improving the model
• Adding Hidden Layer
• Adding Dropout
• Using Adam Optimizer

Learning objective: In this module, you will learn about CNN and explore much about the aspects.

• 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

Learning objective: In this module, you will learn about the concept and working of RCNN and why it came into existence.

• 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

Learning objective: In this module, you will learn about the Boltzmann Machine and its implementation.

• What is the 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

Learning objective: In this module, you will learn about the generative adversarial model and its implementation.

• 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

Learning objective: In this module, you will learn how to classify each emotion into a facial expression.

• Where can you use Emotion & Gender Detection?
• How does it work?
• Emotion Detection architecture
• Face/Emotion detection using Haar Cascades
• Implementation on Colab

Learning objective: In this module, you will learn about the Feedforward network and recurrent neural network and how an RNN network works.

• Where do we use Emotion & Gender Detection?
• How does it work?
• Emotion Detection architecture
• Face/Emotion detection using Haar Cascades
• Implementation on Colab

Learning objective: IN this module, you will learn about the architecture of LSTM and the importance of gates in the LSTM.

• 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 the current time step

Learning objective: In this module, you will learn about how to implement Auto image using pre-trained model inception V3 and LSTM for processing the text.

• Auto Image Captioning
• COCO dataset
• Pre-trained model
• Inception V3 model
• The architecture of Inception V3
• Modify the last layer of the pre-trained model
• Freeze model
• CNN for image processing
• LSTM or text processing