By Scientists for scientists

Deep Learning for Scientists & Engineers

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.

Course Hours20 hours Course DateDecember 12, 2022 Course LocationVirtual

Course Overview

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.

Prerequisite

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.

Lectures

Intro to Deep Learning & Neural NetworksArtificial Intelligence (AI), Machine Learning (ML)
Keras & TensorflowNeural Network Architecture (Layers & Activation Functions)
Building Neural NetworksSequential Model, Compiling & Fitting Models
Tuning Neural NetworksData Partitioning, Model Learning History
Saving & Loading ModelsModel Checkpointing, Transfer Learning
Training Neural Networks IAutomatic Differentiation
Training Neural Networks IICustom Training Loops
Custom ReportingKeras Callbacks
Evaluating Neural Networks IEvaluation Metrics, Diagnostics & Remedies
Evaluating Neural Networks IIModel Appraisal

Instructors

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.

Packages

tensorflow

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.

Alexandre Chabot-Leclerc

Vice President, Digital Transformation Solutions

Tim Diller

Director, Digital Transformation Services

Mark Dickinson

Director, Software Architecture

Eric Olsen

Director, Training Solutions

Glen Granzow

Scientific Software Technical Trainer

Sogo Shiozawa

Scientific Software Developer

Kuya Takami

Senior DTX Services Consultant and Instructor

Logan Thomas

Senior DTX Services Consultant and Instructor