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Learning Neural Networks with Tensorflow [Video]

More Information
  • Work with the Iris Dataset by downloading and visualizing it
  • Install and use Docker
  • Download the data and visualize it
  • Predict the ground state energy of molecules
  • Improve the network by understanding the activation function
  • Work with the MNIST dataset
  • Add pooling layers to reduce your trainable parameters
  • Explore batch normalization

Neural Networks are used all around us: they index photos into categories, translate text, suggest replies for emails, and beat the best games. Many people are eager to apply this knowledge to their own data, but many fail to achieve the results they expect.

In this course, we’ll start by building a simple flower recognition program, making you feel comfortable with Tensorflow, and it will teach you several important concepts in Neural Networks. Next, you’ll start working with high-dimensional uses to predict one output: 1275 molecular features you can use to predict the atomization energy of an atom. The next program we’ll create is a handwritten number recognition system trained on the famous MNIST dataset. We’ll work our way up from a simple multilayer perceptron to a state of the art Deep Convolutional Neural Network.

In the final program, estimate what a celebrity looks like, checking for new pictures to see whether a celebrity is attractive, wears a hat, has lipstick on, and many more properties that are difficult to estimate with "traditional" computer vision techniques.

After the course, you’ll not only be able to build a Neural Network for your own dataset, you’ll also be able to reason which techniques will improve your Neural Network.

Style and Approach

The video is packed with step-by-step instructions, working examples, and helpful advice about building your Neural Network with Tensorflow. You'll learn to build your own network. This practical course is divided into clear byte-size chunks so you can learn at your own pace and focus on the areas of most interest to you. 

  • This extensive course helps you build your network in Tensorflow.
  • This course shows you how to implement the different networks with practical examples
  • It shows you how to solve the most common Neural Network problems with Tensorflow.
Course Length 3 hours 34 minutes
Date Of Publication 28 Nov 2017
The Human Brain and How to Formalize It
Overfitting — Why We Split Our Train and Test Data
Building an Input Pipeline in TensorFlow
Building a Convolutional Neural Network
Batch Normalization
Understanding What Your Network Learned –Visualizing Activations


Roland Meertens

Roland Meertens is currently developing computer vision algorithms for self-driving cars. Previously he has worked as a research engineer at a translation department. Examples of things he has made are a Neural Machine Translation implementation, a post-editor, and a tool that estimates the quality of a translated sentence. Last year, he worked at the Micro Aerial Vehicle Laboratory at the university of Delft, on indoor localization (SLAM) and obstacle avoidance behaviors for a drone that delivers food inside a restaurant. Another thing he worked on was detecting and following people using onboard computer vision algorithms on a stereo camera. For his Master's thesis, he did an internship at a company called SpirOps, where he worked on the development of a dialogue manager for project Romeo.

In his Artificial Intelligence study, he specialized in cognitive artificial intelligence and brain-computer interfacing. His research interests lie in machine learning techniques, human-robot interaction, brain-computer interfaces, and human-computer interaction.