Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data. After the pixels are flattened, the network consists of a sequence of two tf.keras.layers.Dense layers. These are densely connected, or fully connected, neural layers. The first Dense layer has 128 nodes (or neurons).

Time Series forecasting in PBI is based on the thumb technique of smoothening time series prediction called Exponential Smoothening (ES). ES of time series data assigns exponentially decreasing weights for newest to oldest observations. ES is also be used for time series with trend and seasonality. This model is usually used to make short term forecasts, as longer-term forecasts using this technique can be quite unreliable. Collectively, the methods are sometimes referred to as ETS models, referring to explicit modeling for errors, Trend and Seasonality.

In the previous article, we studied Linear Regression. One thing that I believe is that if we can correlate anything with us or our life, there are greater chances of understanding the concept. So I will try to explain everything by relating it to humans.

In the previous article, we studied Deep Learning. One thing that I believe is that if we can correlate anything with us or our life, there are greater chances of understanding the concept. So I will try to explain everything by relating it to humans.

We see this daily -- smartphones recognizing faces in the camera; the ability to search particular photos with Google Images; scanning text from barcodes or book. All of this is possible thanks to the convolutional neural network (CNN), a specific type of neural network also known as strong convnet /strong . Modern deep-learning frameworks like Tensorflow and PyTorch make it easy to teach machines about images, however, there are still questions: How does data pass through artificial layers of a neural network? How can a computer learn from it? One way to better explain a convolutional neural network is to use PyTorch.