A Home where we host engineers to talk about their process and lessons they have learnt on how to make an impact by building cool things
Podcast Overview
A Home where we host engineers to talk about their process and lessons they have learnt on how to make an impact by building cool things
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Publishing Since
5/20/2021
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Recent Episodes
October 2, 2021
Projects - Image classification episode 14
<p><a href="https://ke.linkedin.com/in/bernice-ng-ethe"><strong>Bernice Ngethe</strong></a><strong> reveals how to do image classification. Check out her Twitter handle. </strong> <strong>If you want to read up on some of our research, you can check out all our bonus material over at </strong><a href="https://github.com/JKUATSES/2021-image-classification"><strong>https://github.com/JKUATSES/2021-image-classification</strong></a></p> <p><br></p> <p><strong>Image classification</strong> is pattern recognition in image data using algorithms. Two methods may be used:</p> <p>* Deep learning - uses convolution neural networks to progressively extract higher- and higher-level representations of the image content</p> <p>The CNN comprises a stack of modules, each of which performs three operations.</p> <p>1. Convolution -extracts tiles of the input feature map, and applies filters to them to compute new features, producing an output feature map, or convolved feature (which may have a different size and depth than the input feature map). Convolutions are defined by two parameters:</p> <p>*Size of the tiles that are extracted (typically 3x3 or 5x5 pixels).</p> <p>*The depth of the output feature map, which corresponds to the number of filters that are applied.</p> <p>2. Rectified Linear Unit (ReLU)- the CNN applies a transformation to the convolved feature following each convolution operation, in order to introduce nonlinearity into the model</p> <p>3. Pooling - the CNN downsamples the convolved feature (to save on processing time), reducing the number of dimensions of the feature map, while still preserving the most critical feature information. A common algorithm used for this process is called max pooling.</p> <p>* Transfer learning using pre-trained models</p> <p>In this image classification, both methods were used comparatively and transfer learning had way better performance.</p> <p><strong>REFERENCES</strong></p> <p>* <a href="https://medium.com/analytics-vidhya/image-equalization-contrast-enhancing-in-python-82600d3b371c">https://medium.com/analytics-vidhya/image-equalization-contrast-enhancing-in-python-82600d3b371c</a></p> <p>* <a href="https://www.mygreatlearning.com/blog/introduction-to-image-pre-processing/">https://www.mygreatlearning.com/blog/introduction-to-image-pre-processing/</a></p> <p>* <a href="https://jannik-zuern.medium.com/using-a-tpu-in-google-colab-54257328d7da">https://jannik-zuern.medium.com/using-a-tpu-in-google-colab-54257328d7da</a></p> <p>* <a href="https://towardsdatascience.com/image-enhancement-techniques-using-opencv-and-python-9191d5c30d45">https://towardsdatascience.com/image-enhancement-techniques-using-opencv-and-python-9191d5c30d45</a></p> <p>* <a href="https://machinelearningmastery.com/how-to-control-the-speed-and-stability-of-training-neural-networks-with-gradient-descent-batch-size/">https://machinelearningmastery.com/how-to-control-the-speed-and-stability-of-training-neural-networks-with-gradient-descent-batch-size/</a></p> <p>* <a href="https://developers.google.com/machine-learning/practica/image-classification/convolutional-neural-networks">https://developers.google.com/machine-learning/practica/image-classification/convolutional-neural-networks</a></p> <p>* <a href="https://www.kdnuggets.com/2019/08/introduction-image-segmentation-k-means-clustering.html">https://www.kdnuggets.com/2019/08/introduction-image-segmentation-k-means-clustering.html</a></p> <p>* <a href="https://machinelearningmastery.com/how-to-improve-performance-with-transfer-learning-for-deep-learning-neural-networks/">https://machinelearningmastery.com/how-to-improve-performance-with-transfer-learning-for-deep-learning-neural-networks/</a></p>
July 29, 2021
Projects - Electrical Simulation episode 12
<p><a href="https://github.com/tinegachris"><strong>Tinega Chris</strong></a><strong> reveals how to do electrical simulations for your project. Check out his Twitter handle </strong><a href="https://twitter.com/tinegachris"><strong>@tinegachris</strong></a><strong> </strong> <strong>If you want to read up on some of our research, you can check out all our bonus material over at </strong><a href="https://github.com/JKUATSES/2021-electricalSimulation"><strong>https://github.com/JKUATSES/2021-electricalSimulation</strong></a></p>
July 15, 2021
Projects - Digitals signals simulation episode 11
<p><a href="https://github.com/kelvin169"><strong>Kelvin Mwaniki</strong></a><strong> reveals how to build binary phase-shift keying which can be used by Kenya Power. Check out his Twitter handle </strong><a href="https://twitter.com/mwaniki169?lang=en"><strong>@mwaniki169</strong></a><strong> </strong> <strong>If you want to read up on some of our research, you can check out all our bonus material over at </strong><a href="https://github.com/JKUATSES/2021-DSS">https://github.com/JKUATSES/2021-DSS</a><strong>.</strong></p>
13 total episodes available
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