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Berkeley Global
Discover the flexibility of the powerful TensorFlow package when dealing with heavy financial, mathematical, engineering or scientific problems. Explore the concise and expressive use of TensorFlow advanced package for Python that features many functions and methods for data mining; financial forecasting; investment management; Monte Carlo simulation; statistical testing; pixel classifiers; predator-prey; fluid flow; and various other applications in probability, statistical testing, signal processing and financial forecasting. You also study mathematical operations with array data structures; optimization; the Probability Density Function; interpolation; the Fast Fourier Transform; basic signal processing; and other high-performance benefits that may include the use of some of the core scientific packages such as NumPy, Scipy or Matplotlib. You gain deep understanding and problem-solving experience with this powerful platform.
Prerequisites:
Required
- Proficiency in Python programming
- Knowledge of calculus
- Personal access to Python’s programming environment using TensorFlow
- Personal laptop for classwork/note-taking
Recommended
- Some background on general machine-learning algorithms
- Familiarity with NumPy, SciPy and Matplotlib
- It would be beneficial to have some background on NumPy, SciPy and Matplotlib, which will show general understanding of the platform. Some background on general machine learning algorithms is welcome, but not necessary, as it is covered in the class. Some background in Matlab, calculus, programming; C, C++, or Java is a plus. You must have a personal access to Python’s programming environment using TensorFlow to be able to complete your homework assignments.
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Spring 2025 enrollment opens on October 21!