More Information
Learn
  • Understand what TensorFlow is, how TensorFlow works, from basics to advanced level with case-study based approach.
  • Understand neural networks and how to implement them with TensorFlow via Churn Prediction Case Study.
  • Implement a convolution neural network in TensorFlow for pneumonia detection from the x-ray case study.
  • Implement a recurrent neural network for stock price prediction case study and improving accuracy with long short-term memory network.
  • Learn about TensorBoard for monitoring, transformer, eager execution and debugging code with TensorFlow.
  • Build Transfer learning in Tensorflow using TFlearn via object detection and opinion mining model.
About

Are you eager to deep dive into the details of neural networks and would like to play with it? Do you want to learn Deep Learning Techniques to build projects with the latest Tensorflow 2.0. You may use Keras but it is a high-level implementation which itself uses Tensorflow in the backend and you can’t make changes up to that level in your model as of TensorflowKeras. A good data scientist must have the skill of how things are going on behind the scenes.
This course will help you to be a good Data Scientist by giving hands-on knowledge of Tensorflow 2.0. You will implement real deep learning algorithms and will be available with all the implementation. Using implementation you will learn core details of a neural network like forward-propagation i.e, how to initialize weights and backpropagation i.e, how to update weights with gradient descent algorithm, Cost functions like cross entropy and much more.
By the end of this course, you will be confident to implement your own neural network that is a very amazing thing you are adding to your toolbox.

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

Style and Approach

Our approach is pretty simple and straightforward. In this course, you will be given some introductory part in every section and the advantages and application of that particular topic. After that, we will walk through the code in Python with crisp and clear explanation and easy to understand. Each section will be followed with the Quiz revolve around what we have learned so far, this will make you confident that how proficient you are till now.

Features
  • Understand the complexities of open source library and deep learning with ease and with code implementation in Python.
  • We follow the PEP8 rules for the code implementation which keep you au fait with coding standards and updated tools and techniques like Jupyter notebook and Tensorflow 2.0
  • As Tensorflow 2.0 is in a development Phase so it will cover the most updated content throughout this course.
Course Length 4 hours 4 minutes
ISBN 9781789951707
Date Of Publication 30 Mar 2019

Authors

Akshat Gupta

Akshat Gupta is experienced in Machine learning with more than 3+ years of experience working in the field. He’s currently working as a Machine Learning Engineer at Robofied. He has worked in various domains like Healthcare, Finance, Sales and Automation in Machine learning. He has done various machine learning projects with the Government of India (Ministry of microscale and medium enterprises), with companies and contributed to various open-source projects. He has a strong knowledge of Machine learning and Tensorflow. He’s also very active in research in Machine Learning, he’s has several publications currently going on and writes blogs on Machine Learning.
LinkedIn: https://www.linkedin.com/in/akshat-rg/

Ekta Saraogi

Ekta Saraogi has been a computer engineer for the past 12 years. She started her career as a Java developer. One thing that has always intrigued her is the power of data to drives business outcomes by the right tools. She ventured into the field of data science to get the best out of her technical and business experience and drove and delivered cost-effective business analytics solutions for global businesses. She is now a freelancer, a corporate/online trainer, and a mentor in data science with SAS, R, and Python. She is working on various projects across different domains for global clients across the US and UK. She is also the author of the 'SAS in Practice' video course Published by Packt.