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Implementing Deep Learning Algorithms With TensorFlow 2.0

Implementing Deep Learning Algorithms With TensorFlow 2.0

You’ll learn various Deep Learning approaches such as CNN, RNN, and LSTM and implement them with TensorFlow 2.0.

Overview

Description
Build and train Deep Learning neural networks with TensorFlow 2.0

Deep Learning has caused the revival of Artificial Intelligence. It has become the dominant method for speech recognition (Google Assistant), computer vision (search for "my pictures" on Google Photos), language translation, and even game-related Artificial Intelligence (think AlphaGo and DeepMind). If you'd like to learn how these systems work and maybe make your own, Deep Learning is for you!

In this course, you’ll gain a solid understanding of Deep Learning models and use Deep Learning techniques to solve business and other real-world problems to make predictions quickly and easily. You’ll learn various Deep Learning approaches such as CNN, RNN, and LSTM and implement them with TensorFlow 2.0. You’ll program a model to classify breast cancer, predict stock market prices, process text as part of Natural Language Processing (NLP), and more.

By the end of this course, you’ll have a complete understanding to use the power of TensorFlow 2.0 to train Deep Learning models of varying complexities, without any hassle.

Note that Miniconda and TensorFlow 2.0 installations are required for taking this course.

All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Implementing-Deep-Learning-Algorithms-with-TensorFlow-2.0

About the Author

Harveen Singh Chadha is an experienced researcher in Deep Learning and is currently working as a Self-Driving Car Engineer. He is currently focused on creating an Advanced Driver Assistance Systems (ADAS) platform. His passion is to help people who currently want to enter the Data Science universe. He is the author of the video course Hands-On Neural Network Programming with TensorFlow by Packt Publishing

Course Information

Basic knowledge of Python is assumed

Understand what Deep Learning and TensorFlow 2.0 are and what problems they have solved and can solve
Study the various Deep Learning model architectures and work with them
Apply neural network models, deep learning, NLP, and LSTM to several diverse data classification scenarios, including breast cancer classification; predicting stock market data for Google; classifying Reuters news topics; and classifying flower species
Apply your newly-acquired skills to a wide array of practical and real-world scenarios

This course is for Machine Learning engineers, Deep Learning engineers, and other Data Science professionals.

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Course Specifications

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Adult education is the non-credential activity of gaining skills and improved education. 

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Course duration is 24 hours.

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