Video Analytics Using Deep Learning

1. Deep learning :
Goal: Learn basic manipulation like assigning variables, matrix multiplication, transpose
of matrix, resizing vectors and matrices etc.
Pages: 20
1. Introduction to tensorflow/keras
1.a. Defining Tensors
1.b. Basic operation using tensorflow
1.c. Session logging and Variables
1.d. Tensor Board
1.e. Basic operation using keras

2. Introduction to neural network
Goal : Solve the problem beyond conventional algorithmic approach. In this chapter we will learn about the procedure that is used to compute gradients of a loss function (Backpropagation). We will learn new classification technique called neural network. We will also learn various loss function and optimization methods that helps to measure the quality of parameters and will help to find how much output is agreed with ground truth.
Pages : 50
2.a. Loss function
2.b. Optimization (SGD, RMSPROP, ADAM, Quantum Gradient Descent)
2.c. Backpropagation
2.d. MultiLayer Perceptron
3.e. Lets Build: A classifier on Fashion MNIST to classify the clothes 3. Introduction to convolution neural network
Goal: We will learn new pattern recognition techniques mainly for images that can be used for classification and segmentation.
Pages: 25
FC Vs CNN
3.a. Convolution Layer
3.b. Activation Layer
3.c. Pooling Layer
3.d. Dropout
3.e. Let's classify/recognise object using CNN
etc..

4. CNN architecture
Goal : To learn various deep learning framework with different depth of layers and size of
filter and its use case to increase the efficiency depending on requirements.
Pages: 75
4.a. AlexNet
4.b. Google LeNet
4.c. VGG16 (OxfordNet) architecture
4.d. Let's build model like Face recognition, Emotion analysis using above mentioned
architecture
etc..

5. Image captioning and Generative models
Goal : To learn the action from figure. In this chapter we will learn how to get

Charan Puvvala is a team builder and is technology agnostic. He is a speaker and lead for Hyderabad Machine Learning Group. Currently, he's working on problems related to machine learning and deep learning, natural language understanding, conversational bots, video analytics, big data, data pipelines, and analytics. He has trained over 400 data engineers, data scientists, and software developers in corporate skill upgrade programs. His core competencies are deep learning/artificial neural networks, supervised learning, unsupervised learning, enterprise search architecture, large scale crawling, and natural language processing.