Demystifying the Neural Network Components

This webinar will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This webinar aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand.

Apr 25, 2022, 12:00 – 1:30 PM

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Key Themes

Machine Learning

About this event

This webinar will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This webinar aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand.

We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this webinar we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!

This webinar is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!

This webinar covers a variety of topics, including

NumPy Crash course

Pandas Data Analysis Crash webinar

Data Visualization Crash webinar

Neural Network Basics

TensorFlow Basics

Keras Syntax Basics

Artificial Neural Networks

Densely Connected Networks

Convolutional Neural Networks

Recurrent Neural Networks

AutoEncoders

GANs - Generative Adversarial Networks

Deploying TensorFlow into Production

Keras, a user-friendly API standard for machine learning, will be the central high-level API used to build and train models. The Keras API makes it easy to get started with TensorFlow 2. Importantly, Keras provides several model-building APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlow’s implementation contains enhancements including eager execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines.

TensorFlow 2 makes it easy to take new ideas from concept to code, and from model to publication. TensorFlow 2.0 incorporates a number of features that enables the definition and training of state of the art models without sacrificing speed or performance

It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of webinar, Google!

Speaker

  • Viswas N

Organizers

  • Subhraneel Haldar

    IISC Bangalore

    GDSC Lead

  • Arindam Dandapat

    Design Lead

  • Mriganka Brahma

    BPPIMT STUDENT

    Cloud Lead

  • Satakshi Guha

    Outreach Lead

  • Vidisha Yagnick

    Management Lead

  • Sayandip Bhattacharyya

    Heva

    Tech Lead

  • Anushka Mukherjee

    App Development Lead

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