You’ve seen what deep learning is, why it works, and you’ve implemented tiny nets from scratch. When you’re ready, port these to a proper framework—but now you’ll know exactly what the framework is doing under the hood.
Back to course sections
- Machine Learning
- Introduction to Machine Learning Machine Learning Interview Questions Introduction to Probability and Statistics Introduction to Data Analysis Understanding the Data: Statistics Basic Classification and Machine Learning All About NumPy and Pandas Into the World of Machine Learning Hands-on first Machine Learning Algorithm from Scratch Standardization & Normalization Data Visualization R Vs Python for Machine Learning Advanced Machine Learning Interview Questions Machine Learning Made Easy: Scikit-Learn Machine Learning Made Easy: Scikit-Learn (Part-2: Unsupervised Learning) Introduction to Neural Networks How Do Artificial Neural Networks Work? How Does TensorFlow Work? Practical Introduction to TensorFlow What Is Deep Learning? Deep Learning and Computer Vision Real World Deep Neural Network Examples Practical Exercise: Create background removing application for Zoom/Skype Anomaly Detection In Machine Learning Introduction to Genetic Algorithms in Python A Guide to A/B Testing Getting to Know Decision Trees How Do Large Language Models Work? Univariate, Bivariate, Multivariate Analysis
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