Deep Learning for Scientists & Engineers
Track Machine Learning Track
Gain a practical introduction to deep learning using Keras and TensorFlow with focus on the fundamental model architecture: the neural network.
Deep Learning for Scientists and Engineers provides students with a practical introduction to deep learning using Keras and TensorFlow.
This course focuses on the fundamental model architecture used in deep learning: the neural Network.
We will begin by building a solid foundation of the basics of deep learning and will gradually progress into more advanced topics like model training and Evaluation.
While the course emphasizes a practical approach to deep learning, there are times where just enough theory is covered to understand the why behind certain modeling procedures.
This course requires basic proficiency with Python and the scientific Python stack. Some practical experience with Jupyter Notebooks, NumPy (ndarrays), Pandas (DataFrames), and scientific visualization in Python using Matplotlib are essential to working with the code and concepts presented in this course.
If you have taken Enthought’s Python Foundations for Scientists and Engineers, you have the requisite background knowledge for this course. While not a strict requirement, it is strongly recommended to have taken Enthought’s Machine Learning for Scientists and Engineers (or have a working knowledge of basic machine learning principles) prior to taking this course.
Intro to Deep Learning & Neural Networks – Artificial Intelligence (AI), Machine Learning (ML)
Keras & Tensorflow – Neural Network Architecture (Layers & Activation Functions)
Building Neural Networks – Sequential Model, Compiling & Fitting Models
Tuning Neural Networks – Data Partitioning, Model Learning History
Saving & Loading Models – Model Checkpointing, Transfer Learning
Training Neural Networks I – Automatic Differentiation
Training Neural Networks II – Custom Training Loops
Custom Reporting – Keras Callbacks
Evaluating Neural Networks I – Evaluation Metrics, Diagnostics & Remedies
Evaluating Neural Networks II – Model Appraisal
Enthought instructors have advanced degrees in scientific fields such as physics, engineering, computer science, and mathematics, and all have extensive experience through research and consulting in applying Python to solve complex problems across a range of industries, allowing them to bring their real world experience to the classroom every day.
Download the syllabus for this course here.
Interested in this course?
For more information, contact the Enthought Academy team.
Our Scientific Python Experts
Enthought Academy instructors are scientists and engineers themselves and have deep knowledge and understanding of the strategies and technologies covered in each track, and extensive practical experience applying Python to solve complex challenges across a range of science-based industries.