Topics

• 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

Hands-on/Demo

• Saving and Loading a Model
• Face Detection using OpenCV

Skills

• Image Classification using CNN
• Face Detection using OpenCV

Topics

• 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

Hands-on/Demo

• Transfer Learning
• Object Detection

Skills

• Transfer Learning
• Object Detection
• Mask R-CNN

Topics

• 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

Hands-on/Demo

• Implement RBM
• Simple encoder

Skills

• RBM
• Autoencoders

Topics

• 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

Hands-on/Demo

• Implement Generative Adversarial Network

Skills

• Generative Adversarial Network

Topics

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

Hands-on/Demo

• Implement Emotion and Gender Detection

Skills

• Emotion and Gender Detection

• 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

• Implement COVID RNN GRU

• RNN
• GRU

Topics

• 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

Hands-on/Demo

• Intent Detection using LSTM

Skills

• LSTM
• Sequence Prediction
• Sequence Generation

Topics

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

Hands-on/Demo

• Auto Image Captioning

Skills

• Auto Image Captioning
• CNN for image processing
• LSTM or text processing

Topics

• Why is OpenCV used?
• What is OpenCV
• Applications
• Demo: Build a Criminal Identification and Detection App

Hands-on/Demo

• Build a Criminal Identification and Recognition app on Streamlet.

Skills

• OpenCV
• Project Implementation with OpenCV

Topics

• Use Case: Amazon’s Virtual Try-Out Room.
• Why Deploy models?
• Model Deployment: Intuit AI models
• Model Deployment: Instagram’s Image Classification Models
• What is Model Deployment
• Types of Model Deployment Techniques
• TensorFlow Serving
• Browser-based Models
• What is TensorFlow Serving?
• What are Servables?
• Demo: Deploy the Model in Practice using TensorFlow Serving
• Introduction to Browser based Models
• Demo: Deploy a Deep Learning Model in your Browser.

Hands-on/Demo

• Learn and build a program that Detects Faces using your webcam using OpenCV.
• Learn Hyper parameter tuning techniques in Keras on a Fashion Dataset.
• Build and deploy a model using TensorFlow Serving.
• Build a neural network model for Handwritten digits use activation function, batch size, Optimizer and learning rate for betterment of you model.
• Build a Object detection model and detection is done by providing a video the model accurately identifies the objects that are depicted in the video.

Skills

• Deploying model with Tensorflow

Topics

• Converting text to features and labels
• Multinomial Naive Bayes Classifier
• Leveraging Confusion Matrix

Hands-on/Demo

• Converting text to features and labels
• Demonstrate text classification using Multinomial NB Classifier
• Leveraging Confusion Matri

Skills

• Converting text to features and labels
• Text classification
• Confusion Matrix

Topics

• Sentiment Classification on Movie Rating Dataset

Hands-on/Demo

• Implement all the text processing techniques starting with tokenization
• Express your end to end work on Text Mining
• Implement Machine Learning along with Text Processing

Skills

• Sentiment Analysis