Over the past few years, technologies like artificial intelligence (AI) and machine learning (ML) have been widely adopted and are no longer just “technology buzzwords.” We are beginning to realize the enormous potential of these evolving technologies through a variety of real-life applications like intelligent chatbots, personalized product recommendations, or self-driving cars.
Thanks to its increasing adoption, we are also talking of a connected world – where AI and ML can potentially replace human beings in work environments. How did AI and ML technologies prepare computers to do complex tasks like “learning by example,” – that comes naturally to most human beings?
This is where deep learning is playing a pivotal role. How does deep learning work – and how can you design intelligent models in deep learning with Python programming language? Let us discuss all this in the following sections.
What is Deep Learning?
As a subfield of machine learning and artificial intelligence, deep learning is a technology that is used to design and develop highly accurate data models – using labeled data and neural networks with multiple layers. By using deep learning, a data model can easily classify tasks directly from the text, sound, and images. For example, deep learning can use tens (or even thousands) of layered representations of the data – while other ML techniques may only focus on one or two representations. Hence they are referred to as shallow learning.
How do layered representations work in deep learning? Through models referred to as neural networks – that consist of a structure of literal data layers stacked over each other. Here is an example of how a deep learning model uses multiple layers to identify an image of number 4 and provide the final output.
What Makes Deep Learning so Different and Powerful?
Deep learning uses a technique called representation learning – that differentiates it from other ML techniques or algorithms. How does representation learning work? In simple language, it allows your computer system to analyze raw data and automatically detect the representations – that can be used to classify or detect features. For example, representation learning can be used in complex problems such as:
- Colourising videos or images depending on the background context.
- Using a trained network model – designed for a specific problem – to focus on a completely different problem.
- Identifying individual objects in an image or photograph and generating a textual description of the same.
Using this powerful technique, deep learning has been successful in powering many complex applications. Want to learn deep learning? Let us see where to start.
How Do You Learn Deep Learning?
How do you get started with deep learning? If you possess technical knowledge about various ML algorithms along with an understanding of algebra and statistics, it can help you learn deep learning. However, it is not required.
A much faster and efficient way to design and develop ML models and projects is by using deep learning third-party tools and libraries – developed using the popular Python programming language.
Why do you need to use deep learning in Python programming language? There are multiple benefits of using Python in deep learning, including the following:
- Simple and easy-to-learn language that enables its developers to develop reliable and sophisticated ML models.
- Complete and general-purpose programming language – that is more advanced than either R or MATLAB languages.
- Supports a range of libraries and frameworks that are designed for machine learning like TensorFlow, Theano, Keras, and Scikit.
- Use of a simple programming syntax that promotes faster development and quick testing of ML algorithms.
- As an open-source programming language, Python has a growing community of developers and users, along with online resources and user documentation.
- Recommended for several AI-related use cases, including data analysis, data visualization, computer vision, and natural language processing.
In short, by using deep learning with Python, you can quickly learn to develop and deploy machine learning models that can be used for predictive modeling.
Understanding deep learning in Python
As a developer, you can use your knowledge in Python for deep learning projects – with the help of its Keras library. You can use Keras to create and deploy a neural network in a quick time.
What is the Keras library, and how is it beneficial for deep learning? Written using Python language, Keras is among the leading libraries and APIs for neural networks used in deep learning. Apart from its user-friendly interface, Keras offers several benefits, including:
- Easy to learn and build effective ML models.
- Supports many standalone ML models, including neural networks, cost-related functions, initialization, and regularisation – that can be integrated or reused to create new models.
- Easier to add new modules, classes, and functions.
- Supported on a range of production deployment systems
- Seamless integration with back-end engines including TensorFlow, Theano, and PlaidML
- Integrated as a high-level Keras API in TensorFlow version 2.0
- Backed by technology giants like Microsoft, Amazon, and Apple.
Even though Keras supports multiple back-end engines, its primary or default support is for TensorFlow; in fact, Keras API is packaged as tf.keras API in TensorFlow. However, Keras allows you to easily change your back-end engine – to Theano or CNTK – by simply editing the /.keras/keras.json file in your installation folder.
How to Create a Deep Learning in Python Project Using Keras?
Here is how easily you can create a neural network in deep learning with Python and Keras:
1. Load your dataset
In the first step, you need to define the classes and functions for loading the dataset. For example, you can use the NumPy library to load the data and Keras classes to define the model.
2. Define the Keras neural network model
The next step is to define a sequential model for your neural network and then add multiple layers to it. For this, define the first input layer using the input_dim argument – followed by additional connected layers with the Dense class.
3. Compile the defined Keras model
Next, you can compile the neural network model using a back-end engine like TensorFlow or Theano. During the compiling process, specify some additional properties like the loss function and the optimizer to train the neural network model.
4. Execute the Keras model using data
This step involves executing the neural network model using our loaded data. This can be done using the fit() method on the model.
5. Evaluate the model
The final step is to evaluate and train the model on the complete dataset – along with its network performance. You can do this with the evaluate() function on the model.
With the growing adoption of AI and ML technologies in various applications and industries, there is a severe shortage of skilled and qualified professionals who can customize and implement deep learning projects with Python programming.
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