With the rise of artificial intelligence and machine learning, neural networks have shown great promise for making better investment decisions. For beginners who want to explore neural network investing, there are some free resources available to help get started. This includes introductory online courses on neural networks and machine learning, open source libraries for building neural network models, as well as public financial datasets that can be used for training models.
Learn neural network and machine learning basics
Many free online course providers like Coursera, edX have introductory machine learning and neural network courses that teach the underlying concepts. Going through these courses can build a solid base before applying neural networks for finance.
Get familiar with Python data science libraries
Libraries like NumPy, Pandas, Scikit-Learn, Keras and PyTorch provide all the necessary building blocks for developing neural network models. There are many free tutorials and code samples available for these libraries.
Use public datasets for model training
There are various public datasets like daily stock prices, company fundamentals data available for free. These can be used to train neural network models to predict stock returns without needing to purchase expensive proprietary data.
Start with simple neural network architectures
Instead of directly attempting complex deep learning models, beginners should start with simple feedforward or recurrent neural networks, understand how to tune hyperparameters, before evolving to more advanced architectures.
By leveraging free online courses, open-source libraries and public data, beginners can take the first steps towards applying neural networks for investment analysis. Focusing on fundamentals before quickly jumping to advanced techniques is key.