Tuesday, January 8, 2019

Build Deeper: What's in the Book

We're just 1 day away from the release of my new book Build Deeper: The Path to Deep Learning. So, let's see what I've covered in the book.

Build Deeper: The Path to Deep Learning
Build Deeper: The Path to Deep Learning


The new book is the successor to my earlier book - Build Deeper: Deep Learning Beginners' Guide - (which is why I called this the 'second edition), to which I've added a lot more topics this time. The new book is more than twice the length of the old book, and covers more breadth and depth in Deep Learning.

Here's what you can expect in the book:

Wednesday, January 2, 2019

Happy New Year, with a New Book

2019 is here! Happy New Year everyone!
May we see more exciting development in AI, Machine Learning, and Deep Learning this year.

With this new year, I'm excited to make a new announcement.
My new book "Build Deeper: The Path to Deep Learning" is now completed, and will be releasing very soon.

Build Deeper: The Path to Deep Learning

It's the successor for my earlier book "Build Deeper: Deep Learning Beginners' Guide", and covers everything from an introduction to deep learning, to building your own image recognition and computer vision models from scratch, and advanced topics such as transfer learning and fine-tuning your models, with sample code and step-by-step instructions for everything.

The book will be released within next 2 weeks. Stay tuned for more details.



Thursday, November 22, 2018

Installing the New Anaconda Native TensorFlow Package

For a while now, the most reliable two ways to get TensorFlow installed is to either use the pip package, or compile from source.
Compiling TensorFlow from source takes hours, and still prone to errors (see "Failed Attempts at Building TensorFlow GPU from Source"). While the pip package is relatively easier, getting the GPU version of TensorFlow installed using pip was a hassle.

But not anymore. Because the conda native TensorFlow packages are here now.

Installing is quite easy.

Note: Don't install the pip and conda versions of TensorFlow on the same conda environment. If you already have the pip version installed uninstall it using,

pip uninstall tensorflow


To install the CPU version of TensorFlow, just run,

conda install tensorflow


To install the GPU version,

conda install tensorflow-gpu


Saturday, September 1, 2018

Using Multiple Cameras with OpenCV

As you know, OpenCV is capable of reading from any connected camera in your system, whether it's a built-in webcam (in a laptop) or a USB connected one.

But what if, you wanted to read from more than one cam at the same time?

Can OpenCV handle it?

OpenCV accessing 2 cameras at once
OpenCV accessing 2 cameras at once


Yes, it can!

It's quite simple. Here's how to do it.

Friday, August 31, 2018

Our YouTube Channel is Live!

We have a YouTube Channel now!

https://www.youtube.com/channel/UCRM3zD8oOHP7Epw6XVZ01ew


Don't forget to subscribe!
Fun and exciting Deep Learning experiments, Computer Vision, and Tech Tips too...

Thursday, August 23, 2018

Cleaning up your Anaconda installations

If you've been using Anaconda Python for a while, and been creating multiple environments and adding/removing packages, you may have noticed that it's starting to take up a lot of disk space (sometimes tens of GBs).

Anaconda installation can get big
Anaconda installation can get big


One reason is that anaconda environments are completely isolated workspaces from each other with their own copy of Python. So, the more environments you have, the larger the space needed by anaconda. But the other reason is that anaconda keeps a cache of the package files, tarballs etc. of the packages you've installed. This is great when you need to reinstall the same packages. But, over time, the space can add up.

So, how do we clean up this cache and regain some disk space?

Tuesday, July 10, 2018

Failed Attempts at Building TensorFlow GPU from Source

For the last 3 weeks, I've been trying to build TensorFlow from source. I wanted to get TensorFlow GPU version working on Windows with CUDA 9.2 and cuDNN 7.1. Since the pre-built wheels only work with CUDA 9.0, the only way we can get it working with 9.2 is to build it ourselves from source.

The Windows build of TensorFlow is done using CMake. The official instructions are here: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/cmake

Unfortunately, as I found out after multiple attempts, the build process is not as simple as it sounds.
Every attempt I have made to build it failed so far.

But, I decided to post the steps I took - which didn't work - so that you all may be able use it as a reference if you decided to try it out yourselves. Again, note that these steps did not work.

First, I started with gathering all the dependencies to build on Windows 10:
  • Visual Studio 2015 Community Edition With Update 3 (14.0.25431.01) with C++
  • Anaconda Python 3.6.5
  • Git for Windows 2.18.0
  • Swigwin 3.0.12
  • CUDA Toolkit 9.2
  • cuDNN 7.1
  • CMake 3.11.3