Introduction to Neural Networks

Location:	Lam Research 4650 Cushing Pkwy, Fremont, CA
Date and Time:	May 29, 9am - 5pm May 30, noon - 5pm May 31, 9am - 5pm June 1, 9am - noon
Instructor:	Yoav Ram Postdoctoral fellow at Stanford University. PhD in Mathematical and Computational Biology. Working with Python since 2002. Teaching scientific Python in Academia and Industry since 2011.
Email:	yoav@yoavram.com
Website:	lam.yoavram.com
Syllabus:	Day 1: Scientific Python — NumPy, Matplotlib, Pandas, Seaborn, Scikit-learn, Scikit-image Day 2: From generalised linear models to feed forward networks - generalised linear models implemented in NumPy, maximum likelihood, gradient descent. Day 3: Deep learning with neural networks - feed forward, convolutional and recurrent networks implemented in NumPy and Keras, generative adversarial networks, reinforcement learning, pertained models.
Setup:	Install the Anaconda Python 3.6 Distribution for free. Run the following commands from the terminal/anaconda prompt to make sure everything is ready: `> jupyter notebook`
Preparations:	Please dedicate 30 minutes to go over a Python tutorial. You can skip OOP (section 6) and Advanced (section 7). Take 15 minutes to read about Python’s memory model (if the link does't work, read it here). Dedicate about half an hour to freshen up on some math concepts. A quick read of the first couple of sections of the relevant Wikipedia articles should be enough: normal and binomial distributions, vector algebra, dot product, matrix multiplication, differentiation, chain rule.
Tutorials, Guides & Blogs:	Learn X in Y minutes: Where X=python3. The Hitchhiker’s Guide to Python - a best practices handbook. A gallery of interesting Jupyter Notebooks Programming with Python by Software Carpentry. The ŷhat blog - filter on Python topics. Allen Downey's blog at DSPRELATED.com has many IPython notebooks related to digital signal processing. Scipy Lecture Notes - Tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques. Python Packaging User Guide NumPy for Matlab users
Services:	Crestle - Get a GPU-enabled Jupyter notebook in the cloud, preinstalled with all the relevant Python packages. Binder - turn a GitHub repo into a collection of interactive notebooks. Python tutor - visualize Python's memory.

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