This course is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems.
We start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then we introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems.
Over the rest of the course we introduce and explain several architectures including Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these we explain both the theory and give plenty of example applications.
This course is a good balance between theory and practice. We don't shy away from explaining mathematical details and at the same time we provide exercises and sample code to apply what you've just learned.
The goal is to provide students with a strong foundation, not just theory, not just scripting, but both. At the end of the course you'll be able to recognize which problems can be solved with Deep Learning, you'll be able to design and train a variety of Neural Network models and you'll be able to use cloud computing to speed up training and improve your model's performance.
- Knowledge of Python, familiarity with control flow (if/else, for loops) and pythonic constructs (functions, classes, iterables, generators)
- Use of bash shell (or equivalent command prompt) and basic commands to copy and move files
- Basic knowledge of linear algebra (what is a vector, what is a matrix, how to calculate dot product)
- Use of ssh to connect to a cloud computer
- To describe what Deep Learning is in a simple yet accurate way
- To explain how deep learning can be used to build predictive models
- To distinguish which practical applications can benefit from deep learning
- To install and use Python and Keras to build deep learning models
- To apply deep learning to solve supervised and unsupervised learning problems involving images, text, sound, time series and tabular data.
- To build, train and use fully connected, convolutional and recurrent neural networks
- To look at the internals of a deep learning model without intimidation and with the ability to tweak its parameters
- To train and run models in the cloud using a GPU
- To estimate training costs for large models
- To re-use pre-trained models to shortcut training time and cost (transfer learning)
Data Weekends™ are accelerated data science workshop for programmers where you can quickly learn to apply predictive analytics to real-world data. We offer courses in Data Analytics, Machine Learning, Deep Learning and Reinforcement Learning.
Through our parent company Catalit LLC we also offer corporate training and consulting on Data Science, Machine Learning and Deep Learning.
Data Weekends' founder and lead instructor is Francesco Mosconi, PhD.
Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.
Francesco is a Data Science consultant and trainer. With Catalit LLC he helps companies acquire skills and knowledge in data science and
harness the power of machine learning and deep learning to reach their
Before Data Weekends, Francesco served as lead instructor in Data
Science at General Assembly and The Data Incubator and he was Chief Data
Officer and co-founder at Spire, a YCombinator-backed startup company
that invented the first consumer wearable device capable of
continuously tracking respiration and activity.
He earned a joint PhD in biophysics at University of Padua and
Université de Paris VI and is also a graduate of Singularity University
summer program of 2011.