Learning Path: Diving Into Deep Learning | Simpliv

A comprehensive introduction to Deep Learning with Python

Python is becoming the language of choice for pretty much every arena. It is a very simple yet an extremely powerful programming language. One of the field where Python is extensively used is data science. Deep learning—a field in data science—is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python. Starting off with core Python coverage and swiftly moving to practical deep learning content, this Learning Path will have you crunching stats and quoting facts in no time at all. By the end of this Learning Path, you can start working with deep learning right away.

About the Author

• William Fiset is a Mathematics and Computer Science Honors student at Mount Allison University with in interest in competitive programming. William has been a Python developer for +4 years, starting his early Python experience with game development. He owns a popular YouTube channel that teaches Python to beginners and the basics of game development.
• Daniel Arbuckle holds a Doctorate in Computer Science from the University of Southern California, where he specialized in robotics and was a member of the nanotechnology lab. He now has more than ten years behind him as a consultant, during which time he’s been using Python to help an assortment of businesses, from clothing manufacturers to crowdsourcing platforms. Python has been his primary development language since he was in High School. He’s also an award-winning teacher of programming and computer science.
• Eder Santana is a PhD candidate on Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, Eder contributes to Keras: Deep Learning Library for Python. Besides deep learning, he also likes data visualization and teaching machine learning, either on online forums or as a teacher assistant.