Engineering AI Agents
BOOK
Foundations
Training Deep Networks
Perception
State Estimation
Large Language Models
Multimodal Reasoning
Planning
Markov Decision Processes
Reinforcement Learning
COURSES
Introduction to AI
AI for Robotics
Deep Learning for Computer Vision
DATA MINING - BEING PORTED
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Training Deep Neural Networks
Training Deep Neural Networks
Building Training Pipelines
Author
Pantelis Monogioudis
Training Deep Netwoks
Deep Neural Networks
Introduction to Backpropagation
Backpropagation in Deep Neural Networks
Backpropagation DNN exercises
Fashion MNIST Case Study
Regularization in Deep Neural Networks
Batch Normalization
Introduction to Transfer Learning
Transfer Learning for Computer Vision Tutorial
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Backpropagation DNN exercises
Computational graph in Tensorboard showing the components involved in a TF BP update
Backpropagation in Deep Neural Networks
Following the introductory section, we have seen that backpropagation is a procedure that involves the repetitive application of the chain rule. Let us now treat its…
Deep Neural Networks
DNNs are the implementation of
connectionism
, the philosophy that calls for algorithms that perform function approximations to be constructed by an interconnection of…
Fashion MNIST Case Study
This is a case study on MNIST Fashion dataset that due to the almost perfect classification of MNIST is now the new point of reference dataset to learn and try ML algorithms…
Introduction to Backpropagation
In case you have forgotten the basics of calculus, please review the calculus section before proceeding.
Introduction to Transfer Learning
Transfer Learning is a foundational approach to learning.
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