Introduction to Deep Learning
The course is part of this learning path
Introduction to Deep Learning
In this Zero to Deep Learning course has been expertly created to provide you with a strong foundation in machine learning and deep learning. So whether you're just starting out with your practice of machine learning or you're a more experienced data scientist that is adding deep learning to the mix, this course will provide the necessary skills to serve as a solid foundation for you to continue learning after the course has been completed.
The course will start out covering simple data and structured data, then moving onto images, with sound, with text, and more complex data where deep learning comes to life. By the end of the course, you'll be able to recognize which problems can be solved with deep learning and organize data in a way that can be used by a neural network. Understanding and learning how to build a neural network model, including fully connected, convolutional, and recurrent neural network and train a model using cloud computing, all within this course.
This course is made up of 5 cohesive lectures that start off the journey into Data and Machine Learning with Cloud Academy.
- Learn the key principles of data and machine learning
- Come away with a strong foundation of the subject in order to develop new skills further
- Understanding and learning how to build neural network models
- No prior knowledge of data and machine learning required
About the Author
I am a Data Science consultant and trainer. With Catalit I help companies acquire skills and knowledge in data science and harness machine learning and deep learning to reach their goals. With Data Weekends I train people in machine learning, deep learning and big data analytics. I served as lead instructor in Data Science at General Assembly and The Data Incubator and I was Chief Data Officer and co-founder at Spire, a Y-Combinator-backed startup that invented the first consumer wearable device capable of continuously tracking respiration and activity. I earned a joint PhD in biophysics at University of Padua and Université de Paris VI and graduated from Singularity University summer program of 2011.
-Maybe you like watching videos at high speed and a lot of them together. Or maybe you like pacing your study throughout the week, watching one video at a time. In either case, this course is designed to minimize your effort to follow it. The material is divided in sections, and each section is less than one hour long. Also, each section only deals with one topic, and it comes with an exercise at the end. So you can proceed in two ways. Either you can watch all the videos of that section and then do the exercise to check that you've really understood what I've explained in the videos, or you can do the reverse: start from the exercise and go back and check the videos whenever you feel you're not confident on how to solve a problem.
Some videos will contain slides and a voiceover. Some will be screencasts where I show you how to do things. And some will be me talking and introducing new topics. The exercises come with a code that already works and some questions. The questions expand on the code that already works. But they're provided for you to really test your knowledge. We also provide you with the solution to the exercises. We encourage you not to check it before you've tried. In this section, we will talk about application of deep learning, and we'll make sure that your system is set up with all the necessary software to follow the course. So let's get started with section one, introduction.