curiousily/Deep-Learning-For-Hackers
Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)
This resource provides practical guidance and code examples for building machine learning models using Python, TensorFlow, and Keras. It takes raw data, such as images, text, or time-series data, and shows you how to process it to build predictive models for tasks like forecasting, classification, and anomaly detection. This is ideal for developers who want to learn how to apply deep learning techniques to real-world problems.
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Use this if you are a Python developer looking to build practical machine learning applications and want to learn how to use TensorFlow 2 and Keras with hands-on examples.
Not ideal if you are looking for a conceptual overview of machine learning theory without any coding or practical implementation details.
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Jupyter Notebook
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MIT
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Last pushed
Apr 23, 2020
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