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The Lattice Boltzmann Method: Principles and Practice

This book is an introduction to the theory, practice, and implementation of the Lattice Boltzmann (LB) method, a powerful computational fluid dynamics method that is steadily gaining attention due to its simplicity, scalability, extensibility, and simple handling of complex geometries. The book contains chapters on the method's background, fundamental theory, advanced extensions, and implementation.

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Physics-based Deep Learning Book

This document contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations.

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A parallel Fortran framework for neural networks and deep learning

Enables you to build dense, fully connected neural networks of arbitrary shape and size

  • Backprop with Mean Square Error cost function

  • Data-based parallelism

  • Several activation functions

  • Support for 32, 64, and 128-bit floating point numbers

Github Repo

Upcoming Podcast

For the upcoming podcast I am very happy to welcome Dominique Madier to my show!

Dominique is a senior aerospace consultant with more than 20 years’ experience and advanced expertise in Finite Element Analysis of static and dynamic problems for linear and nonlinear structural behaviors.

He is the author of the FEA book "Practical Finite Element Analysis for Mechanical Engineers", a book about the best practical methods and guidelines for the development and validation of finite element models. It gives to the structural engineers the keys to developing accurate and reliable finite element models.

In this podcast, we talked about:

  • The difference between FEA & FEM 🤔

  • FEA concepts like non-linearity & Degrees of Freedom📚

  • Dominique’s book on FEA 📙

  • The FEA academy 🎓

  • Most common mistakes in FEA ❓

  • Verification, Validation, the future of FEA 📊

  • and much more…

Went live

Course of the Week - Machine Learning (Coursera)

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include:

  1. Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).

  2. Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).

  3. Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Access Course.

Book of the Week

Deep Learning with Python, Second Edition

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You’ll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

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Visualize Ideas

"Believe You Can and You're Halfway There."
– Theodore Roosevelt

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Closing Remarks

If you have any wishes for video topics, you can submit them in this form. Apart from that you can always reach out to me via Twitter or Instagram or LinkedIn, so let’s connect!

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See you next week and in the meantime, make sure to keep engineering your mind!