TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
This video addresses common commercial machine learning problems using Google’s TensorFlow library. It will not only help you discover what TensorFlow is and how to use it, but will also show you the unbelievable things that can be done in machine learning with the help of examples/real-world use cases. We start off with the basic installation of Tensorflow, moving on to covering the unique features of the library such as Data Flow Graphs, training, and visualization of performance with TensorBoard—all within an example-rich context using problems from multiple sources.. The focus is on introducing new concepts through problems that are coded and solved over the course of each section.
About The Author
Shams Ul Azeem is an undergraduate of NUST Islamabad, Pakistan, in Electrical Engineering. He has a great interest in the field of computer science and has started his journey from Android Development.Now he’s pursuing his career in Machine Learning, particularly in Deep Learning, by doing medical-related freelance projects with different companies.
He was also a member of RISE lab, NUST, and has a publication in the IEEE International Conference, ROBIO as a co-author on Designing of motions for humanoid goal keeper robots.
Get the user excited about ML; a quick view of various platforms and key products using the ML tools (photo search, Siri, and so on).
We will install the TensorFlow platform and associated prerequisites on the Mac OS X operating system.
Since TensorFlow does not function natively on Windows, we will cover Virtual Machine setup so Windows users can run Linux on a VM.
We will cover TensorFlow installation on Linux. This also covers installation on Windows via a Virtual Machine running Linux.
Which dataset do we use for our first classifier, and what attributes do we consider? Let's use a dataset of letters in numerous typefaces.
We just spent a good amount of time obtaining and preparing data, are we expected to do this for each run? No, you'll now learn to automate the initial steps used to feed the machine learning process.
How do we represent images internally within the machine learning environment? We'll hold image data in a stack of matrices.
So now that we have looked upon the major concepts, the question remains how do we actually train the classifier in code? We are now going to code and train a classifier using TensorFlow.
Once training is complete, how are the results interpreted and measured? We'll use the training set, with known labels to measure how well our trained system performs.
How do we monitor the internals of our training setup and execution? We'll use TensorBoard to view our network architecture and probe values through the training.
How do we monitor the scalars and tensors on TensorBoard? We'll set up code for log specific variables and TensorBoard reads these structured logs.
How do we view the network architecture we've built for our training? We'll push the graph to a TensorBoard readable log and view it on Graph Explorer.
How do we actually apply what you've just learned to our own project? You'll learn to go through individual changes to our previous project.
How do we process images with more complex and better suited neural networks? For this purpose, we will use the well-known convolutional neural networks also known as CNNs.
How do CNNs work and what does their model look like? We are going to talk about the basic model architecture of CNN.
How to implement a basic CNN? We will look into the coding of a CNN.
How does it look after coding? We will visualize the implemented CNN in TensorFlow.
How much better CNNs are as compared to normal fully connected networks? We will compare the performances of the two models on CIFAR-10 dataset.
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