Deep learning is the next big thing. Its favorable results in applications with huge and complex data is remarkable. Deep learning 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. Developers can apply deep learning techniques to accomplish complex machine learning tasks, and train AI networks to develop deep levels of perceptual recognition.
This comprehensive 2-in-1 course will help you explore and create intelligent systems using deep learning techniques. You’ll understand the usage of multiple applications like Natural Language Processing, bioinformatics, recommendation engines, etc. where deep learning models are implemented. You’ll get hands on with various leep learning scenarios and get powerful insights from your data.
Contents and Overview
This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Deep Learning with Python, covers concepts that will help you dive into the future of data science and implement intelligent systems using deep learning with Python. Through this course, you’ll learn convolutional, recurrent neural networks and build up the theory that focuses on supervised learning and integrate into your product offerings such as search, image recognition, and object processing. Finally, you’ll start working with deep learning right away. This course will make you confident about its implementation in your current work as well as further research.
The second course, Deep Learning with R, covers powerful, independent videos to build deep learning models in different application areas using R libraries. This course will teach you how to leverage deep learning to make sense of your raw data by exploring various hidden layers of data. Each section in this course provides a clear and concise introduction of a key topic, one or more example of implementations of these concepts in R, and guidance for additional learning, exploration, and application of the skills learned therein.
By the end of this training program you’ll start working with deep learning right away. Starting out at a basic level, you’ll have learned how to develop and implement intelligent systems with deep learning algorithms using Python and R in real world scenarios.
About the Authors
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.
Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of ContinuousAI an open source project aiming to connect people and reorganize resources in the context of Continuous Learning and AI. He is also the PhD students' representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: A comparison between CNNs and HTMs on object recognition tasks".
