Image classification with Imagenet and Resnet50

ResNet-50 is a 50-layer convolutional neural network with a special property that we are not strictly following the rule, that there are only connections between subsequent layers. So ResNet is using so called residual learning, the actual layers are skipping some connections and connecting to more downstream layers to improve performance. But details on that is beyond the scope of this course and luckily this class already provides us with a prepared and compiled model. The initial lectures series on this topic can find in the below links: Introduction to Anomaly Detection https://www.youtube.com/watch?v=IFHX4HUAo1w&list=PLpW3QouFxOnM6YWVOrcUBaQiy8EWi05pi&index=37

How to implement an anomaly detector (1/2) https://www.youtube.com/watch?v=DN0H2Qz3Rxg How to implement an anomaly detector (2/2) https://www.youtube.com/watch?v=nYZuQg5K22Y How to deploy a real-time anomaly detector https://www.youtube.com/watch?v=LnPrT-IkzNw Introduction to Time Series Forecasting https://www.youtube.com/watch?v=G7_uNCOFEzE Stateful vs. Stateless LSTMs https://www.youtube.com/watch?v=R7CwkhZYJdU Batch Size! which batch size is to choose? https://www.youtube.com/watch?v=wfyErdPsZPI Number of Time Steps, Epochs, Training and Validation https://www.youtube.com/watch?v=tsprdX9RkRg Batch size and Trainin Set Size https://www.youtube.com/watch?v=5kLLKhNJlEY Input and Output Data Construction https://www.youtube.com/watch?v=zCHrQRlu688 Designing the LSTM network in Keras https://www.youtube.com/watch?v=Y3ApYArvBr8 Anatomy of a LSTM Node https://www.youtube.com/watch?v=WrA3LlKAbf0 Number of Parameters:How LSTM Parmeter Num is Computed. https://www.youtube.com/watch?v=oXNBR0U1A54 Training and loading a saved model. https://www.youtube.com/watch?v=jKjm1cX-mtM ... https://www.youtube.com/watch?v=jeXf3U9UTrM