A high-level language, used in web development, data science, automation, AI and more. Known for its readability, which means code is easier to write, understand and maintain. Backed by library support, so we don’t have to build everything from scratch, there’s probably a library that already does what we need.
Built-in Data Types In programming, data type is an important concept.
Variables can store data of different types, and different types can do different things.
Python has the following data types built-in by default, in these categories:
Text Type: str Numeric Types: int, float, complex Sequence Types: list, tuple, range Mapping Type: dict Set Types: set, frozenset Boolean Type: bool Binary Types: bytes, bytearray, memoryview None Type: NoneType
- Numpy
- Pandas
- Matplotlib
- Scikit-learn
- TensorFlow
- PyTorch
- OpenCV
- Keras
List of DeepLearning LibrariesHere’s a concise list of popular deep learning libraries widely used for building and training neural networks:
- TensorFlow: Open-source library by Google, versatile for deep learning and machine learning tasks, with strong support for production deployment.
- PyTorch: Developed by Meta AI, favored for its dynamic computation graph and ease of use in research.
- Keras: High-level API (now integrated with TensorFlow) for rapid prototyping and experimentation.
- JAX: Google’s library for high-performance numerical computing, increasingly popular for deep learning with its functional approach.
- MXNet: Apache’s scalable deep learning framework, known for efficiency in distributed training.
- Hugging Face Transformers: Specialized for NLP, offering pre-trained models like BERT and GPT for easy fine-tuning.
- ONNX: Open Neural Network Exchange, for model interoperability across frameworks.
- Caffe: Focused on speed, particularly for convolutional neural networks in computer vision.
- Deeplearning4j: Java-based, designed for JVM environments and enterprise use.
- FastAI: Built on PyTorch, simplifies training with high-level abstractions for quick results.