Video Analytics Using Deep Learning: Building Applications with Tensorflow, Keras, and Yolo (Paperback)

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 textual description of an image.In this chapter we will also learn Unsupervised learning and in particular Generative models. Given sample X and Y as input and output we will learn way to sample these X, Y pairs means from input data we can generate different types of probabilistic data. We will learn to generate new image, given input image Pages: 90 5.a. RNN 5.b. LSTM 5.c. Let's find what picture tells us (Gesture Recognition or Traffic vision). 5.d. Density estimation and its types (how data is distributed, identifying hidden structure of data) 5.e. Generative adversarial network 5.f. Pixel RNN/CNN 5.g. Variational Autoencoders 5.h. With given input let's generate random faces using generative model