V Kishore Ayyadevara - Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots

V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has 10 years' experience in data science, solving problems to improve customer experience in leading technology companies. In his current role, he is responsible for developing a variety of cutting edge analytical solutions that have an impact at scale while building strong technical teams.

Prior to this, Kishore authored three booksPro Machine Learning Algorithms, Hands-on Machine Learning with Google Cloud Platform, and SciPy Recipes.

Kishore is an active learner with keen interest in identifying problems that can be solved using data, simplifying the complexity and in transferring techniques across domains to achieve quantifiable results.

, Building a Feedforward Neural Network , will teach you about the basics of building a neural network by building a feedforward network from scratch and looking at backward propagation in Python. Additionally, you will be learning about the various components of building a neural network in Keras.

, Building a Deep Feedforward Neural Network , will teach you about the various hyperparameters that impact the accuracy of neural networks by having you implement multiple models on a handwritten digit dataset.

, Applications of Deep Feedforward Neural Networks , will see you appreciate the flexibility of neural networks by having you implement models to predict the likelihood of a customer defaulting on a loan, identify the sentiment of a text review, and classify audio signals. Additionally, you will learn about fine-tuning a neural network to assign higher weightage to certain rows and certain classes of output.

, Building a Deep Convolutional Neural Network , will explore how CNNs work by having you implement one from scratch in Python. You will also learn about how CNNs help in improving prediction accuracy when images are translated. Finally, you will learn about classifying gender of person in image along with improving network accuracy through data augmentation.

, Transfer Learning , will look at leveraging pre-trained models, such as VGG16, VGG19, ResNet50, Inception architectures that were built on millions of images, based on which we fine-tune the weights of our model to further improve its accuracy. Finally, you will learn about detecting the locations of key points in the image of a face.

, Detecting and Localizing Objects in Images , will teach you about the various components of building a model that detects the location of objects in an image. Additionally, you will build a region proposal-based algorithm and an anchor box-based algorithm from scratch to identify the location of objects in image.

, Image Analysis Applications in Self-Driving Cars , will teach you about building a U-Net architecture so that you can perform semantic segmentation on images to group similar objects in an image of a road together. Additionally, you will be performing analysis to identify the angle at which a car needs to be steered based on the image of a road.

, Image Generation