PhD in computational science, 15 years’ experience with Python, and 7 years’ experience teaching scientific Python in Academia and Industry.
Software developers and engineers interested in a pratctial introduction to deep learning.
An introduction to data processing, analysis, and visualization with Python combined with a primer in deep learning with neural networks.
Two days, 16 hours of interactive face-to-face sessions (not a webinar).
Instruction with code. Built-in exercises and problems. Laptops open with source code throughout all sesssions.
Coding in Python, inside interactive Jupyter notebooks, using TensorFlow, the deep learning library developed by Google, and Keras.
Understanding the math behind neural networks using Python and its scientific stack.
Training and using neural networks using Python, TensorFlow, and Keras.
Experience in software development (not necessarily Python) and a basic understanding of statistics, linear algebra, and calculus (BSc in exact sciences or similar).
Install the Anaconda Python 3.6 Distribution for free. Run the following command from the terminal to make sure everything is ready:
python -c "import tensorflow as tf;print(tf.Session().run(tf.constant('Hello, World!')))"
Do some preliminary reading (2-3 hours) and exercises (3-4 hours after each day) at home or the office.
I'm a postdoctoral fellow at Stanford University with a PhD in Mathematical & Computational Biology from Tel Aviv University. I've been teaching Python in Academia and Industry for the past 7 years, including at Tel Aviv University, Applied Materials, and KLA-Tencor.
I enjoy solving and communicating complex problems and ideas. I develop and deliver Python programming workshops focused on numerical, scientific, and statistical applications in Israel, California, and anywhere else I'm needed.
For more details and offerings click here.