Deep Learning with Keras
Deep Learning is one of the most demanding fields these days because it has the hottest topics like robotics, AI, ML, image processing, etc. It is a part of Machine learning and AI that contains various networks that have the capability to learn about unsupervised data. It works on the algorithms that are contained by the structures and functions. Deep Learning is a technique that teaches machine about the natural working of humans. Day-by-day, there is an increasing demand for Deep Learning. Let us learn about Deep Learning with Keras.
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Why Keras for Deep Learning?
Keras is built to increase efficiency while working with the Deep Learning models. It is an easy-to-use Python library that enables the implementation of Deep Learning models to fast and effectively. It enhances fast experimentation by user-friendly, modular, and extensible API. Keras allows fast and simple prototyping. It promotes simple and intuitive syntax.
Keras follows four principles: Modularity, User-friendliness, Extensibility, and Working with Python. These four principles enable Keras to perform efficiently with Deep Learning. The powerful weapon of Keras is the ideology of the models. Keras is the best tool in the toolbox any data scientist working with neural networks. The main advantage of using Keras for Deep Learning is that it enables you to start with neural networks in an easy way.
Basics of Deep Learning with Keras
- Initially, you should have knowledge about Artificial Neural Network. Deep learning is a field of Machine learning. It is basically a set of algorithms. In ML, these structures are enhanced by the functions and the structures of the brain. The algorithms that this field use is termed as an Artificial Neural Network. Perceptron is the basic unit of Neural Network.
- You should know about Python libraries that will help you to analyze and visualize the data.
- Understanding the data: It will be easy and convenient if you understand the data first, then load it. Study in-depth about the data. This will help you further to work with the data.
- Loading the data: This task becomes easy with the very famous Python Library. Pandas library will help you to load the data easily. It is a data manipulation Python Library. To read the data in the CSV file, make use of read_csv() function.
- Exploring the data: This will help to check whether the data you import was successful or not. For this Pandas provides you many functions like head(), tail(), and sample(). These functions will provide you an easy way to inspect your data.
- Visualizing the data: Make a proper scatter plot to see the correct co-relation. This will help you to distribute the variables of the datasets. You can use the histogram() function for this.
- Training and testing of data: This is an important task. You should test and train your data correctly. Make sure that your model is trained according to your requirements. You can import the train_test_split from sklearn.model_selection and assign the data and the target labels to the variables X and y.
- Compiling the data and fitting the data: Use Compile() function and fit() function for this step. While compiling, use adam optimizer. In the loss() function, use binary_crossentropy as argument.
- Predict target values: Use predict() function for this. You can make predictions for the labels of the test set with it.
You must validate your model correctly.
Building Deep Learning Models with Keras
As discussed above, Keras focuses on the idea of Models. The sequence model is the first and most famous models. It is organized in a fashion that follows a linear stack of layers. It offers you an interesting way of building deep learning models. You can create and add layers to it. It is not ordered dependent. You can add the layers in the order you want the computation to perform. After defining the model, you need to compile the model.
While compilation, you can make use of optimizers and loss functions. After the completion of the compilation, the model is ready for the data. It is time to perform computation. You can either compute one piece of data or feed the system with entire training data. After training it, you can use the model to make predictions about the new data. Now, your model is ready to deal with the newest data possible.
So, to build a Deep Learning Model, follow the steps:
- Definition of the model: You initially need to create a sequence. Then ass subsequent layers to it.
- Compilation of the model: Compilation is a step that deals with the optimizers and loss functions. While compiling, you need to use the framework that is capable to optimize the computation task.
- Fitting the model: Make sure that the model for your requirement. To check its fitness, execute the model using data. The model must fit the data.
- Compose predictions: Now your model if fit to deal with new data. Hereby, make use of the model to perform predictions on new data.
You can also create a model using Functional API. It allows you to create the same model. But comparatively, it is more flexible. It offers more simplicity and readability. This is mostly useful when you have to deal with multiple inputs and output layers.
Deep Learning is a field that has ample career opportunities. It is the most popular field with a promising career. It makes use of Keras. Although, TensorFlow and Theano are also compatible with Deep Learning. But Keras is easy and fast than other libraries. Keras focuses on fast experimentation. It is a high-level API for neural network models. Keras suits the best of Deep Learning. SO this was all about Deep Learning with Keras.