Udemy One Stop Guide to Deep Learning with TensorFlow: 5-in-1 Udemy
Price: USD 200
  • Location: Online
  • Duration: Flexible

    Course details

    Are you short on time to start from scratch to use deep learning to solve complex problems involving topics like neural networks and reinforcement learning? If yes, then this is the course perfect for you!

    Tensorflow is Googles popular offering for machine learning and deep learning. It has become a popular choice of tool for performing fast, efficient, and accurate deep learning. TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning.

    Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible.

    This comprehensive 5-in-1 course is a step-by-step guide and your go-to guide for being a deep learning expert at your organization. It will evaluate common and not so common deep neural networks with the help of insightful examples that you can relate to and show how these can be exploited in real world with complex raw data. This course presents the implementation of practical, real-world projects, teaching you how to leverage Tensforflows capabilities to perform efficient deep learning.

    Contents and Overview

    This training program includes 5 complete courses, carefully chosen to give you the most comprehensive training possible.

    The first course, Deep Learning with TensorFlow, covers the power of deep learning with Google's TensorFlow! With this video course, you will dig your teeth deeper into the hidden layers of abstraction using raw data. This course will offer you various complex algorithms for deep learning and various examples that use these deep neural networks. Train your machine to craft new features to make sense of deeper layers of data. During the video course, you will come across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, high level interfaces, and more.

    The second course, Hands-on Deep Learning with TensorFlow, covers building smart systems with ease using TensorFlow. This course is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow. All these modules are developed with step by step TensorFlow implementation with the help of real examples.By the end of the course you will be able to develop deep learning based solutions to any kind of problem you have, without any need to learn deep learning models from scratch, rather using TensorFlow and its enormous power.

    The third course, TensorFlow Deep Learning Solutions for Images, covers TensorFlow's capabilities to perform efficient deep learning on Images. In this video, you will be acquainted with the different paradigms of performing deep learning such as deep neural nets, convolutional neural networks, recurrent neural networks, and more, and how they can be implemented using Tensorflow. This will be demonstrated with the help of end-to-end implementations of three real-world projects on popular topic areas such as natural language processing, image classification, fraud detection, and more. By the end of this course, you will have mastered all the concepts of deep learning and their implementation with Tensorflow and Keras.

    The fourth course, Tensorflow Solutions for Text, covers use of deep learning to make predictions on email. This volume introduces working with text, with a focus on the most plentiful source of text out there: email. Working with email text from your own Gmail account, you will build up a label predictor, similar in effect to the technology Google uses to power the Social and Promotions tabs. With this technique, you will be able to build your own email classification and automated workflow hooks.

    The fifth course, Tensorflow Solutions for Data, covers Tensorflow's capabilities to perform efficient deep learning on Data sets. In this video you'll work with categorical data to predict loan performance. Categorical, structured data often appears in spreadsheets and relational databases, common data sources in business. This technique can be used to effectively predict performance or detect potential fraud. You will also work with recurrent neural networks, which generate realistic test and placeholder data. This is useful to fill in systems with synthetic test data to simulate load and test the breadth of a working system and predict one column from the others.

    By the end of the course, youll channel the power of deep learning with Google's brainchild TensorFlow to develop smart systems from scratch!

    About the Authors

    • Dan Van Boxel is a data scientist and machine learning engineer with over 10 years of experience. He is most well-known for Dan Does Data, a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research articles and presented findings at the Transportation Research Board and other academic journals.


    • Salil Vishnu Kapur is a Data Science Researcher at the Institute for Big Data Analytics,Dalhousie University. He is extremely passionate about Machine Learning, Deep Learning, Data mining and Big Data Analytics. Salil has around 3 years of experience working with these technologies as a Senior Analyst in Capgemini, prior to that as an intern at IIT Bombay through the FOSSEE Python TextBook Companion Project and presently with the Department of Fisheries and Transport Canada through Dalhousie University.


    • Will Ballard is the chief technology officer at GLG, responsible for engineering and IT. He was also responsible for the design and operation of large data centers that helped run site services for customers including Gannett, Hearst Magazines, NFL, NPR, The Washington Post, and Whole Foods. He has also held leadership roles in software development at NetSolve (now Cisco), NetSpend, and Works(now Bank of America).

    Updated on 14 November, 2018
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