Deep Learning with Python and Keras (Tensorflow 2)


TensorFlow and Keras today are one of the popular frameworks for building, training and deploying your deep learning models. Deep Learning with Python and Keras (Tensorflow 2) training aims to provide a comprehensive overview of the TensorFlow 2.0 Ecosystem and the Keras API.

After completing the training you will be able to understand different TensorFlow2.0 features like eager computation, Autograph, tf.Datasets, and distributed training. You will learn to use TensorFlow Keras API will be used to build complex models. You will also get familiarization with the entire TensorFlow Ecosystem like TF Hub, TensorFlow Serving, Federated Learning using TF, TensorFlow.js and AutoML.

Session Outline:

Lesson 1: Understanding the Keras API
In this lesson, you will learn how to use keras API to build models of varying degree of complexity. You will gain understanding of different loss functions. The automatic differentiation performed by the TensorFlow backend. And how to use callbacks to get better insight into the model training.

Lesson 2: Complex model building and training
In this lesson, the focus will be on building complex models with multiple inputs and outputs. You will also learn how to use TF Hub and tf.Keras.Applications to use pretrained models. The lesson will talk about how you can build your own loss function and use it to train a model.

Lesson 3: Bringing it all together
The focus of this lesson is to introduce you to different components of the TensorFlow Ecosystem and see how they can be used for deploying and building AI solutions.

Background Knowledge:

Python and knowledge of calculus


Amita Kapoor, is the author of best-selling books in the field of Artificial Intelligence and Deep Learning. She mentors students at different online platforms such as Udacity and Coursera and is a research and tech advisor to organizations like DeepSight AI Labs and MarkTechPost. She started her academic career in the Department of Electronics, SRCASW, the University of Delhi, where she was an Associate Professor. She has over 20 years of experience in actively researching and teaching neural networks and artificial intelligence at the university level. A DAAD fellow, she has won many accolades with the most recent being Intel AI Spotlight award 2019, Europe. An active researcher, she has more than 50 publications in international journals and conferences. Extremely passionate about using AI for the betterment of society and humanity in general, she is ready to embark on her second innings as a digital nomad.

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