How to Quickly Build Deep Learning Applications with Python

Deep learning is one of the most discussed topics in data science. Thanks to breakthroughs in the technology, significant progress has been made, and deep learning has gained widespread attention. It is predicted that more deep learning applications will impact our lives in the future, and this influence has already begun.

If you are outside the field, deep learning may seem intimidating. Terms like TensorFlow, Keras, and GPU computing can be confusing. However, deep learning is not as difficult as it may appear. While keeping up with cutting-edge research requires significant time, using existing tools to solve practical problems can be very efficient.

Interestingly, in applying deep learning, I rediscovered a childlike curiosity and joy of exploration. This article introduces 6 deep learning applications you can build quickly. They may seem complex at first, but with available resources, you can implement them in a short time. These cases demonstrate the practical value of deep learning and help you understand how it works.

Table of Contents

1. Applications Using Existing APIs
   • Pros and Cons of Deep Learning APIs
   • Colorizing Photos with Algorithmia API
   • Building a Chatbot with Watson API
   • News Aggregator with Sentiment Analysis (Aylien API)
2. Open-Source Applications
   • Pros and Cons of Open-Source Code
   • Sentence Correction with Deep Learning
   • Portrait Style Transfer
   • Deep Reinforcement Learning Bot for Flappy Bird
3. Other Valuable Resources

1. Applications Using Existing APIs

An API (Application Programming Interface) is essentially a program running on a remote server that can be called locally via the internet. This is similar to using a Bluetooth speaker: even though your computer has built-in speakers, you can use an external device via a wireless connection. The core value of an API is that someone has already implemented the complex parts, allowing you to call it to solve problems quickly.

First, let's analyze the pros and cons of using APIs to build applications.

1.1 Pros and Cons of Deep Learning APIs

Pros:

• Deep learning applications often require significant GPU power and data storage/processing capabilities. With an API, you can leverage the service provider's high-performance computing resources without setting up a complex local environment.
• Your local system does not bear the computational burden, resulting in low resource consumption.
• Easy to integrate new features and quickly expand application capabilities.

Cons:

• Building and maintaining an API is costly, requiring ongoing development and operational resources.
• Dependent on network connectivity; unstable connections can cause service interruptions.
• Security risks exist, requiring measures like authentication and access rate limiting.

Now, let's practice with a few specific API applications.

1.2 Colorizing Photos with Deep Learning (Algorithmia API)

Automatic image colorization has been a popular area in computer vision. Converting black-and-white photos to color sounds like science fiction. It's similar to teaching a child to color a picture, but teaching AI to do it is challenging. Humans accumulate color knowledge through long-term observation, but modeling this association for AI is not easy.

Recent research shows that neural networks trained on large datasets can predict and generate plausible colors from grayscale images.

We will use the colorization API provided by Algorithmia to implement this.

Requirements:

• Python 2 or 3
• Internet connection (to call the API)
• Algorithmia account (registration grants 5000 credits; each call costs about 12 credits)

Implementation Steps:

1. Register and get an API key: Register on the Algorithmia website and get your API key from your profile page.
2. Install the Algorithmia client: Run the following command in your terminal:

   pip install algorithmia

3. Upload an image: Upload your black-and-white image to the Data folder provided by Algorithmia.
4. Write the calling code: Create a local Python file (e.g., colorize.py) and write the following code (replace with your image path and API key):

   import Algorithmia
   
   input = {
       "image": "data://your_username/datacollect/your_image.jpg"
   }
   client = Algorithmia.client('your_api_key_here')
   algo = client.algo('deeplearning/ColorfulImageColorization/1.1.5')
   result = algo.pipe(input)
   print(result)

5. Run the program: Execute python colorize.py in the terminal. The processed color image will be automatically saved to the Data folder.

You have now completed a simple image colorization application!

1.3 Building a Chatbot (Watson API)

IBM Watson is a prominent representative in the field of artificial intelligence, with natural language processing as one of its core capabilities. We will use Watson's Conversation service (now Assistant) to build a simple chatbot.

Requirements:

• Python 2 or 3
• Internet connection
• IBM Cloud account (offers a free trial)

Implementation Steps:

1. Register and create a service: Register on IBM Cloud, create a "Watson Assistant" service in the catalog, and obtain credentials (username, password, workspace ID).
2. Install the SDK: Run in the terminal:

   pip install watson-developer-cloud

3. Write the code: Create a Python file (e.g., chatbot.py), copy the following code, and replace the credentials:

   import json
   from watson_developer_cloud import AssistantV1
   
   assistant = AssistantV1(
       username='YOUR_SERVICE_USERNAME',
       password='YOUR_SERVICE_PASSWORD',
       version='2021-06-14'
   )
   
   workspace_id = 'YOUR_WORKSPACE_ID'
   
   response = assistant.message(
       workspace_id=workspace_id,
       input={'text': 'Hello'}
   )
   print(json.dumps(response, indent=2))

4. Run the test: Execute python chatbot.py to see the conversation response from Watson.

You can define dialogue flows and intents in the Watson Assistant console to build more complex conversation logic.

1.4 News Aggregator with Sentiment Analysis (Aylien API)

Using natural language processing, we can filter news based on sentiment, for example, showing only positive messages. The Aylien News API provides this capability.

Requirements:

• Python 2 or 3
• Internet connection
• Aylien account (free registration)

Implementation Steps:

1. Register and get keys: Register on the Aylien website and get your App ID and API Key from the console.
2. Install the SDK: Run:

   pip install aylien-news-api

3. Write the code: Create a Python file (e.g., news_filter.py) and use the following code example (replace your credentials):

   import aylien_news_api
   from aylien_news_api.rest import ApiException
   
   configuration = aylien_news_api.Configuration()
   configuration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = 'YOUR_APP_ID'
   configuration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = 'YOUR_API_KEY'
   
   api_instance = aylien_news_api.DefaultApi(aylien_news_api.ApiClient(configuration))
   
   opts = {
       'language': ['en'],
       'published_at_start': 'NOW-7DAYS',
       'published_at_end': 'NOW',
       'sentiment_title_polarity': 'positive',
       'per_page': 10
   }
   
   try:
       api_response = api_instance.list_stories(**opts)
       for story in api_response.stories:
           print(story.title)
   except ApiException as e:
       print("Exception: %sn" % e)

4. Run the program: Execute python news_filter.py to output positive news headlines from the past week.

By adjusting query parameters, you can implement more complex news filtering logic.

2. Open-Source Applications

Open-source culture has greatly advanced the deep learning community. Researchers share code and models, accelerating the pace of technological iteration. Below are a few applications based on open-source code.

2.1 Pros and Cons of Open-Source Applications

Pros:

• Transparent code; you can view implementation details and customize as needed.
• Community collaboration drives continuous project improvement; tested by many, offering higher stability and usability.

Cons:

• Lack of official support; it may be difficult to get timely help when problems arise.
• There may be license restrictions; commercial use requires attention to compliance.

Note: When using open-source projects, it is recommended to carefully read the official documentation and GitHub repository instructions, as many projects are still under active development and interfaces may change.

2.2 Sentence Correction with Deep Learning

Traditional spell checkers can correct typos, but fixing grammatical errors is more challenging. Deep learning-based text correction models can improve this.

We will use an open-source project based on a sequence-to-sequence (Seq2Seq) model trained on a corpus containing grammatical errors and their corrected sentences.

Example:
Input: Kvothe went to market
Output: Kvothe went to the market

Requirements:

• Python 2 or 3
• TensorFlow (GPU acceleration recommended for training)

Implementation Steps:

1. Install TensorFlow and clone the repository:

   pip install tensorflow
   git clone https://github.com/atpaino/deep-text-corrector.git
   cd deep-text-corrector

2. Download the dataset: Download the Cornell Movie-Dialogs corpus and extract it into the project directory.
3. Preprocess the data:

   python preprocessors/preprocess_movie_dialogs.py --raw_data movie_lines.txt --out_file preprocessed_movie_lines.txt

   This will generate training, validation, and test files.

4. Train the model:

   python correct_text.py --train_path ./movie_dialog_train.txt --val_path ./movie_dialog_val.txt --config DefaultMovieDialogConfig --data_reader_type MovieDialogReader --model_path ./movie_dialog_model

   Training time depends on hardware configuration.

5. Test the model:

   python correct_text.py --test_path ./movie_dialog_test.txt --config DefaultMovieDialogConfig --data_reader_type MovieDialogReader --model_path ./movie_dialog_model --decode

Model performance will improve with more training data and algorithm optimization.

3. Other Valuable Resources

In addition to the above cases, there are many excellent platforms and tools to help you quickly build deep learning applications:

• Google Colab: Provides free GPU computing resources, suitable for running Jupyter Notebooks for prototype development.
• Hugging Face Transformers: Offers many pre-trained natural language processing models for tasks like text classification and generation.
• Fast.ai: Provides high-level APIs and practical courses, enabling developers to quickly implement cutting-edge deep learning models.
• Model Zoo: Model libraries for major frameworks (e.g., TensorFlow, PyTorch) containing many pre-trained models supporting transfer learning.

The world of deep learning is vast and fascinating. I hope the cases and resources provided in this article help you get started quickly and turn ideas into practical applications.