Deep-Learning-For-Hackers

Why we included this project

For developers who learn by building rather than by working through theory, this collection of Jupyter notebooks offers a practical route into deep learning with TensorFlow 2 and Keras. Each chapter centers on a real, everyday problem such as heart disease prediction, cryptocurrency price forecasting, or sentiment analysis, and pairs it with working code you can run in Colab or on your own machine. The tutorials walk through the full practical cycle, from data preparation and handling imbalanced datasets to fixing underfitting and overfitting, tuning hyperparameters, and serving a trained Keras model behind a Flask API. It makes no claim to being a complete textbook, but as a hands-on introduction spanning time series, computer vision, and NLP in one place, it gives newcomers a solid base before they move on to harder projects.