Turning the page
The new year is round the corner and so are the thoughts about a quest into the hidden layers of Deep Learning. This year’s goal was to become a developer and it was achieved as planned on time. The main projects were in Android and the end of the year was under the sign of Machine Learning and ,more precisely speaking, Deep Learning.
So what are the main points in almost a two month headlong journey on the Deep Learning highway?
Deep Learning Book
- As you should have already known by now Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville was published. This detailed and helpful book on foundations of artificial neural networks is pretty expensive but can be accessed electronically in html format for free.
Jason Brownlee’s Machine Learning Mastery site comes in handy
- As for me I started to be interested in the field of Machine Learning and Deep Learning in November, 3rd 2016 when I found out about the books of Jason Brownlee on Machine Learning. I bought a number of his books such as Machine Learning with Python, Deep Learning with Python and Machine Learning Algorithm From Scratch with Python. Not only that all those books are very practical and goal oriented but Jason is also a nice person who answers questions and is cooperative regarding errata for the books.
Blog Posts Ignited by Deep Learning Sparks
- First post was about using open source machine learning TensorFlow library. It explained how to install it on Linux and run an image to caption model.
- Second post was about the quest to the Banana Classifier with OpenCV. It describes a zigzag path to building android app using OpenCV (Code in GitHub included ).
- The third one was about predicting possible future applications of Deep Learning. It appears it was really interesting for readers since more than 200 people read it around the globe (it is about 4 times more than regularly).
- The recent one was about installing Keras, Theano and TensorFlow (all open source tools) on Linux and Windows.
Here And There
- Check out Brandon Rohrer an easy to grasp explanation of ConvNets and try to spot a confusion matrix in one of the slides.
- Read Franceso Gadaleta Ahem Detector with Deep Learning blog post that will explain you how to get rid of ‘Ahem’ sound in podcasts. This Jupyter notebook may come in handy.
- Read Henry W. Lin and Max Tegmark arxiv paper on Why does deep and cheap learning work so well
- Watch Ruslan Salakhutdinov lecture at Deep Learning School on Foundations of Unsupervised Deep Learning
- Check the latest blog posts and papers worth investing your time in the filed of Deep Learning on Twitter/ LinkedIn
I hope you’ll find at least some of the links helpful.
Best wishes for the upcoming year and set the goals to achieve in 2017. Have a clear plan to get going and jump Deeply into Learning.