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:
Tutorials, Guides & Blogs:
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.

# Topic Notebook
1 Numerical Python: NumPy numpy.ipynb
2 Plotting with Python: Matplotlib matplotlib.ipynb
3 Data analysis: Pandas and Seaborn pandas-seaborn.ipynb
4 Image processing image-processing.ipynb
5 Machine learning with scikit-learn scikit-learn.ipynb
6 Linear model linear-model.ipynb
7 Logistic model logistic-model.ipynb
8 Softmax model softmax-model.ipynb
9 Linear model: object detection object-detection.ipynb
10 Feed Forward Network FFN
11 Feed Forward Network: Keras K_FFN.ipynb
12 Feed Forward Network: TensorFlow TF_FFN.ipynb
13 Convolutional Neural Network CNN.ipynb
14 Convolutional Neural Network: TensorFlow and Keras TF_CNN.ipynb
15 Inference with pre-trained models pretrained.ipynb
16 Recurrent Neural Network RNN.ipynb
17 Generative Adversarial Network GAN.ipynb
18 Reinforcement Learning reinforcement.ipynb

Code of Conduct

Based on:

Contributor Covenant Code of Conduct


Built with:

Code of Conduct Builder

Encouraged Behaviour

  • Using welcoming and inclusive language
  • Being respectful of differing viewpoints and experiences
  • Gracefully accepting constructive criticism
  • Showing empathy towards other workshop members

Unacceptable Behaviour

  • The use of sexualized language or imagery and unwelcome sexual attention or advances
  • Trolling, insulting/derogatory comments, and personal or political attacks
  • Public or private harassment
  • Publishing others' private information, such as a physical or electronic address, without explicit permission
  • Other conduct which could reasonably be considered inappropriate in a professional setting

How can I report a violation of the Code of Conduct?

  • Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the instructor at yoav@yoavram.com or submitting a (possibly anonymous) message at this form.