Developer Guides and Projects

 The financial sector is one of the most data-rich industries, and AI has emerged as a powerful tool to extract insights, predict trends, and automate decision-making. However, training AI models with financial data comes with unique challenges—ranging from data sensitivity and regulatory compliance to the complexities of time-series analysis. This guide will walk you through the process, providing clear steps and best practices to help you build robust AI models tailored for financial applications. 1. Define Your Objectives Begin by clarifying what you want to achieve with your AI model. In finance, potential applications include: Risk Assessment: Predicting credit defaults or market risks. Forecasting: Anticipating stock prices, market trends, or economic indicators. Algorithmic Trading: Creating strategies based on historical patterns. Fraud Detection: Identifying anomalous activities in transactions. Portfolio Optimization: Allocating assets for maximum return wit...

 This is getting started guide for ML beginners who wants to use Jupyter Notebook on Docker environment. Docker is a way to use different development environments and frameworks without physically install them on your PC or workstation. For more information, please visit docker website.

  Hugging Face Hugging face is a machine learning community where you can share pre trained models and explore other trained models, which are suited for many use cases. Many of the models already trained well and ready to use. Without delay get started! Python setup Open your Jupiter notebook or for easy startup without python installation on local machine, use Colab . In this example we are using mistralai/Mistral-7B-v0.1 model. Let's install the transformers module which is the primary dependency for HuggingFace.   conda install transformers Following script will work like a charm . # mistrel model test from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mistral-7B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) text = "Hello my name is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.d...

  How to load an image in Keras for machine learning from a url ?

 In Keras Sequential Model problems we can get predicted result as output. As a beginner you may note that it is not what you expected. So how do we get the actual label for that image.?