Navigating Deep Learning Frameworks: Exploring Models on Kaggle platform
In this article, we will discuss the Deep Learning frameworks that are available in the models section of the Kaggle platform and the models that are suitable for these frameworks. The frameworks section is dominated by popular options such as TensorFlow and PyTorch.
Kaggle is a popular online platform for data science and machine learning enthusiasts. It provides a range of resources and tools for data scientists, including datasets, competitions, notebooks, and a community of data professionals. The “Models” section in Kaggle refers to a specific area of the platform where users can share and explore machine learning models, codes, and projects.
In the “Models” section of Kaggle, you can find:
1. Machine Learning Models: Users upload and share their machine learning models, which can include code, data preprocessing steps, and documentation. These models cover various topics and domains, from image classification to natural language processing and more.
2. Notebooks: Many of these models are presented in the form of Jupyter notebooks. These notebooks contain code, explanations, and visualizations that demonstrate how a particular machine learning task or project was accomplished.
3. Tutorials and Documentation: Users often provide detailed explanations and documentation along with their models, making it easier for others to understand and replicate their work.
4. Frameworks and Libraries: The models in this section may use different machine learning frameworks like TensorFlow, PyTorch, or scikit-learn. Users can choose models that align with the frameworks they prefer.
The available frameworks for this platform include TensorFlow2, Pytorch, and TensorFlow1.
TensorFlow 2 is an open-source machine learning framework that was developed by Google as a successor to TensorFlow 1. Its main focus is on improving ease of use, flexibility, and performance.
Some of the key features of TensorFlow 2 include eager execution by default, which makes it easier to develop and debug models. Additionally, it integrates the Keras API as its high-level neural network API, making it more user-friendly for building deep learning models.
Another advantage of TensorFlow 2 is that it provides dynamic computation graphs, making it simpler to work with dynamic input shapes and control flow operations. It also offers support for both CPU and GPU acceleration, as well as distributed computing environments.
As a result of its numerous benefits, TensorFlow 2 is widely used in various machine learning and deep learning applications such as computer vision, natural language processing, and reinforcement learning.
PyTorch is a machine learning framework that is open-source and highly popular. It is primarily developed by Facebook’s AI Research lab (FAIR) and has gained a reputation for its dynamic computational graph which makes working with variable-sized inputs and dynamic network architectures much easier.
Researchers and practitioners prefer PyTorch’s dynamic nature due to its flexibility and ease of debugging. The framework’s simplicity and Pythonic approach have led to its strong presence in the deep learning community, and it has a large and active community that contributes to its development and provides a wealth of resources and libraries.
PyTorch also supports GPU acceleration, and distributed computing, and includes extensive libraries for tasks like computer vision, natural language processing, and reinforcement learning. It is a popular choice for research projects, especially in the academic and research community.
Google’s TensorFlow 1 was a significant milestone in deep learning frameworks, popular for its scalability and deployment support. However, the static computation graph was less intuitive and harder to debug. A steeper learning curve compared to PyTorch led to the development of TensorFlow 2 with a more user-friendly approach.
To summarize, TensorFlow 2 and PyTorch are two of the most commonly used deep learning frameworks nowadays. TensorFlow 2 offers the best of both worlds by providing ease of use through Keras integration and dynamic computation graphs. On the other hand, PyTorch is known for its dynamic graph and simplicity, which makes it a popular choice for research and rapid prototyping.
Models compatible with Tensorflow and Pytorch Frameworks
It provides access to various models that comply with these frameworks. You can review user-generated notebook works specific to these models or create your own.
Some of the most commonly used models are:
ResNet_v2 (Residual Network Version 2): ResNet is a deep convolutional neural network architecture known for its depth. ResNet_v2 is an improved version that addresses the vanishing gradient problem. It’s widely used in image recognition tasks and has various depths (e.g., ResNet-50, ResNet-101).
The key difference compared to ResNet V1 is the use of batch normalization before every weight layer.
This TF-Hub module uses the TF-Slim implementation of resnet_v2_50 with 50 layers. The module contains a trained instance of the network, packaged to do the image classification that the network was trained on. If you merely want to transform images into feature vectors, use the module google/imagenet/resnet_v2_50/feature_vector/1 instead, and save the space occupied by the classification layer.
Flan-T5: Flan-T5 is a variant of the T5 (Text-To-Text Transfer Transformer) model, which is a pre-trained transformer model for natural language understanding tasks. It’s designed for text generation, translation, summarization, and other NLP tasks. These are the checkpoints released as part of the paper Scaling Instruction-Finetuned Language Models. They were initialized from the T5 1.1 LM-Adapted and instruction-finetuned.
Llama 2: Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama-2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.
MobileNet_v2: MobileNet_v2 is a neural network architecture designed for efficient on-device vision applications. It’s known for its lightweight and fast performance, making it suitable for mobile and embedded devices.
MobileBERT: MobileBERT is a compact version of the popular BERT (Bidirectional Encoder Representations from Transformers) model. It’s designed for efficient natural language understanding on mobile devices while maintaining high performance.
MobileBERT is further trained on SQuAD, or Stanford Question Answering Dataset which is a reading comprehension dataset consisting of articles from Wikipedia and a set of question-answer pairs for each article.
The MobileBERT Q&A model takes a passage and a question as input, then returns a segment of the passage that most likely answers the question. It requires semi-complex pre-processing including tokenization and post-processing steps that are described in the BERT paper and implemented in the sample app.
BERT (Bidirectional Encoder Representations from Transformers): BERT is a pre-trained transformer-based model that has revolutionized natural language processing tasks. It’s known for its bidirectional context understanding and is used for tasks like text classification, named entity recognition, and question answering.
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