Python Tutorial: Understanding sequential models

Want to learn more? Take the full course at https://www.datacamp.com/courses/machine-translation-in-python?embedded=true&slug=machine-translation-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Here, you will learn about the machine learning model used to implement the encoder and the decoder of the machine translator. A sentence is a time-series input which means that every word in the sentence is affected by previous words. The encoder and the decoder use a machine learning model that can learn from time-series or sequential inputs like sentences. The machine learning model is called a sequential model. Sequential models go from one input to the other while producing an output at each time step. During time step 1, the first word is processed and during time step 2, the second word is processed. The same model processes each input. You will be using a type of sequential models called a gated recurrent unit, or GRU, in your translator. For example, the inputs to the encoder is a sequence of English words encoded as one-hot vectors. Let's consider an example. At time equals 1, the GRU model takes in the input word "We" and some initial hidden state which are all zeros. Then the model produces a new hidden state 0.8 and 0.3. In the next time step, the GRU model sees the next word "like" and the previous hidden state 0.8 and 0.3. The GRU takes in these two inputs to produce a new hidden state, and continues this way to the end of the sentence. The hidden state obtained from the previous step acts as memory of what the model has seen previously. These hidden states are computed using the internal parameters of the GRU model which are learned during the model training. Let's quickly revisit the Keras functional API. Keras has two important objects: Layers and Models. You can define an input layer using the Input object. You can also define a hidden layer like a GRU layer using th