Discover deep learning and machine learning with Python and TensorFlow
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
It can be hard to get started with machine learning, particularly as new frameworks like TensorFlow start to gain traction across enterprise companies. TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
This Learning Path begins by covering a mastery on Python with a deep focus on unlocking Python’s secrets. We then move on to understand deep learning as implemented by Python and TensorFlow. Finally, we solve common commercial machine learning problems using TensorFlow.
If you have no prior exposure to one of the most important trends impacting how we do data science in the next few years, this Learning Path will help you get up to speed.
The goal of this Learning Path is to help you understand deep learning and machine learning by getting to know Python first and then TensorFlow.
This Learning Path is authored by some of the best in their fields.
About the Authors
Daniel Arbuckle got his Ph.D. In Computer Science from the University of Southern California. He has published numerous papers, along with several books and video courses, and is both a teacher of computer science and a professional programmer.
Eder Santana is a Ph.D. candidate in Electrical and Computer Engineering. 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, the deep learning library for Python. Besides deep learning, he also likes data visualization and teaches machine learning, either on online forums or as a teacher assistant.
Dan Van Boxel
Dan Van Boxel is a data scientist and machine learning engineer with over 10 years of experience. He is well-known for "Dan Does Data", a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research and presented findings at the Transportation Research Board and other academic journals.
Shams Ul Azeem
Shams Ul Azeem is an undergraduate student of NUST Islamabad, Pakistan, in Electrical Engineering. He’s pursuing his career in machine learning, particularly in deep learning, by doing medical-related freelance projects with different companies.
- Requires a firm understanding of Python and the Python ecosystem
- Build Python packages to efficiently create reusable code
- Become proficient at creating tools and utility programs in Python
- Design and train a multilayer neural network with TensorFlow
- Understand convolutional neural networks for image recognition
- Create pipelines to deal with real-world input data
- Set up and run cross domain-specific examples (economics, medicine, text classification, and advertising)
- Learn how to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage
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