While large language models (LLMs) have been widely applied to code generation, they struggle with generating entire deep learning projects, which are characterized by complex structures, longer functions, and stronger reliance on domain knowledge than general-purpose code. An open-domain LLM often lacks coherent contextual guidance and domain expertise for specific projects, making it challenging to produce complete code that fully meets user requirements. In this paper, we propose a novel planning-guided code generation method, DLCodeGen, tailored for generating deep learning projects. DLCodeGen predicts a structured solution plan, offering global guidance for LLMs to generate the project. The generated plan is then leveraged to retrieve semantically analogous code samples and subsequently abstract a code template. To effectively integrate these multiple retrieval-augmented techniques, a comparative learning mechanism is designed to generate the final code. We validate the effectiveness of our approach on a dataset we build for deep learning code generation. Experimental results demonstrate that DLCodeGen outperforms other baselines, achieving improvements of 9.7% in CodeBLEU and 3.6% in human evaluation metrics.

In this article, I will present you the deep learning project that I wanted to perform, then I'll present the techniques and approach that I used to tacle this. And I will end up that article with some meaningful reflections, that I hope would help some of you. I wanted to build a smartphone app which can recognize flower from taken picture. Basically the app is splitted into two parts, the front-end part which is basically the mobile development. I wanted to build from scratch a deep learning model without deep learning framework, to help me understand the inner working process of image classification (I know it sounds crazy).

Why is it called deep learning? Deep learning is a type of machine learning that uses artificial neural networks with multiple layers to learn from large amounts of data. The term "deep" refers to the depth of the neural network, which means the number of layers it has. The term "deep learning" was coined in the mid-2000s to differentiate this type of neural network from the earlier shallow neural networks that were primarily used for simpler tasks such as pattern recognition. Where is deep learning used?

Building a machine learning workstation can be difficult, not to mention choosing the right workstation with the proper machine learning memory requirements. There are a lot of moving parts based on the types of projects you plan to run. Understanding machine learning memory requirements is a critical part of the building process. Sometimes, though, it is easy to overlook. The average memory requirement is 16GB of RAM, but some applications require more memory.

Goal of this project was to develop and train a Deep learning model capable of detecting and covering license plates of vehicles. This project was implemented with Python using the Keras framework on top of Tensorflow. A number of python libraries like OpenCV, Numpy, Pandas, etc… were used. A lot of work done in order the reduce the overall size of the code and model weights to allow a deployment in Serverless environments like AWS Lambda and Google Cloud Functions. Finally i was able to deploy on Google Cloud Function with an API in front.

Deep Learning Projects Using TensorFlow 2: Neural Network Development with Python and Keras [Silaparasetty, Vinita] on Amazon.com. *FREE* shipping on qualifying offers. Deep Learning Projects Using TensorFlow 2: Neural Network Development with Python and Keras

We have listed the top deep learning project you should look at before moving on to the final project.

Natural language processing (NLP) is continuing to grow in popularity, and necessity, as artificial intelligence and deep learning programs grow and thrive in the coming years. Natural language processing with PyTorch is the best bet to implement these programs. In this guide, we will address some of the obvious questions that may arise when starting to dive into natural language processing, but we will also engage with deeper questions and give you the right steps to get started working on your own NLP programs. Interested in a deep learning workstation that can handle NLP training? First and foremost, NLP is an applied science.

Natural language processing (NLP) is continuing to grow in popularity, and necessity, as artificial intelligence and deep learning programs grow and thrive in the coming years. Natural language processing with PyTorch is the best bet to implement these programs. In this guide, we will address some of the obvious questions that may arise when starting to dive into natural language processing, but we will also engage with deeper questions and give you the right steps to get started working on your own NLP programs. First and foremost, NLP is an applied science. It is a branch of engineering that blends artificial intelligence, computational linguistics, and computer science in order to "understand" natural language, i.e., spoken and written language.

Neural networks, or deep learning, is a capability that is changing the way people live and work. From language translations to medical diagnosis to speech recognition to self-driving cars, deep learning is in the fabric of a technology revolution. But what is deep learning, and how much knowledge does a nontechnical or computer science stakeholder need to have to contribute to or run projects, or to spot opportunities for applications? How do healthcare executives know the potential data objectives faced can be addressed with deep learning? To add more complexity, the marketplace is filled with content and claims that will confuse even the most ardent expert.