SK Reddy

With global commerce on the rise, collaboration between individuals speaking different languages is essential. The need for real time and accurate Language translation using deep networks is picking up fast.

Historically, language translation was done using rules and examples. This is obviously not scalable and does not cater to the evolution of languages. Later word-based and phrase-based translations were invented that still could not address the need of the hour.

The modern day translations are done using deep networks. I would like to discuss the topic of language translation and deep learning using Tensorflow. The attendees will get a chance to work hands-on to create the building blocks of Language Translator using Tensorflow.

Abstract: Many additional questions compound the already complicated process of product management when we introduce AI to the mix. Artificial intelligence (AI) comes with diversity in models, frameworks, tools, techniques, best practices, etc. AI product management is also afflicted with issues like availability of talent, awareness of AI fundamentals in the organization, middle management block-hole towards AI, etc.

I will discuss about product management in my startup on how it was conceived, developed, released and how the company was eventually successfully acquired. I will talk about my experience as a founder, as a CPO and also as a developer of the product in a startup context. In addition, I will share my product management experiences in a big corporation. I will also compare & contrast the experiences of a PM in a large corp vis-à-vis PM in a start-up.

Looking at an image and having to describe the contents of the image gives a rich set of opportunities to humans to describe it in multiple ways. Not just the names of the objects in the image, but the relative position and proportion of the images could be explained in different ways. In addition, the task gets even diverse when one has to explain the details of interaction between objects in the image.

The modern day Image captioning solutions include Multimodal RNN, pre-trained CNN models on large Image datasets and the required image datasets.

I will discuss the fundamentals of Neural Networks, talk about text processing using RNNs, explain the current state-of-the-art in pre-training CNN models on large image datasets and share the details of how to create a complex model to train and infer on images & text.

Detecting the image of an object is difficult.
Looking at an image and having to describe the contents of the image gives a rich set of opportunities to humans to describe it in multiple ways. Not just the names of the objects in the image, but the relative position and proportion of the images could be explained in different ways. In addition, the task gets even diverse when one has to explain the details of interaction between objects in the image. The initial solution of hard coded visual concepts and sentence templates would not work and also are restrictive.

The modern day Image captioning solutions include Multimodal RNN, pre-trained CNN models on large Image datasets and the required image datasets.

I will discuss the fundamentals of Neural Networks, talk about text processing using RNNs, explain the current state-of-the-art in pre-training CNN models on large image datasets and share the details of how to create a complex model to train and infer on images & text.