Adding Manning Publication's books and one course to respective lists
diff --git a/books.md b/books.md
index f78cc02a..d888ebfb 100644
--- a/books.md
+++ b/books.md
@@ -31,10 +31,14 @@ The following is a list of free, open source books on machine learning, statisti
 * [Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage) Book+MatlabToolBox
 * [R Programming for Data Science](https://leanpub.com/rprogramming)
 * [Data Mining - Practical Machine Learning Tools and Techniques](http://cs.du.edu/~mitchell/mario_books/Data_Mining:_Practical_Machine_Learning_Tools_and_Techniques_-_2e_-_Witten_&_Frank.pdf) Book
+* [Machine Learning with TensorFlow](https://www.manning.com/books/machine-learning-with-tensorflow) Early access book
+* [Reactive Machine Learning Systems](https://www.manning.com/books/reactive-machine-learning-systems) Early access book
 
 ## Deep-Learning
 
 * [Deep Learning - An MIT Press book](http://www.deeplearningbook.org/)
+* [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python) Early access book
+* [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) Early access book
 
 ## Natural Language Processing
 
@@ -42,6 +46,7 @@ The following is a list of free, open source books on machine learning, statisti
 * [NLTK](http://www.nltk.org/book/)
 * [NLP w/ Python](http://victoria.lviv.ua/html/fl5/NaturalLanguageProcessingWithPython.pdf)
 * [Foundations of Statistical Natural Language Processing](http://nlp.stanford.edu/fsnlp/promo/)
+* [Natural Language Processing in Action](https://www.manning.com/books/natural-language-processing-in-action) Early access book
 
 ## Information Retrieval
 
diff --git a/courses.md b/courses.md
index 4ca92faa..c5eb843b 100644
--- a/courses.md
+++ b/courses.md
@@ -13,3 +13,4 @@ The following is a list of free or paid online courses on machine learning, stat
 * [Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120) - free
 * [Probabilistic Graphical Models (by Prof. Daphne Koller, Stanford)](https://www.coursera.org/specializations/probabilistic-graphical-models) Coursera Specialization or [this Youtube playlist](https://www.youtube.com/watch?v=WPSQfOkb1M8&list=PL50E6E80E8525B59C) if you can't afford the enrollment fee.
 * [Reinforcement Learning Course (by David Silver, DeepMind)](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLzuuYNsE1EZAXYR4FJ75jcJseBmo4KQ9-) - YouTube playlist and [lecture slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html).
+* [Keras in Motion](https://www.manning.com/livevideo/keras-in-motion) $

Hello,

I was wondering if you would consent to adding the books and course that I have added to the respective resource lists on your page.

Thanks for your consideration.