Deep Learning (Training)

Colleagues, the Deep Learning program will equip you to drive advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment. Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website. Working knowledge of Python, NumPy, pandas and familiarity with calculus and linear algebra is recommended. Training modules with hands-on projects include: 1) Introduction - apply style transfer to your own images, and gain experience using development tools such as Anaconda and Jupyter notebooks, 2) Neural Networks - build your first network with Python and NumPy (Project: Predicting Bike-Sharing Patterns), 3) Convolutional Neural Networks - build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them (Project: Landmark Classification & Tagging for Social Media), 4) Recurrent Neural Networks - build your own recurrent networks and long short-term memory networks with PyTorch (Project: Generate TV Scripts), 5) Generative Adversarial Networks - implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs (Project: Generate Faces), and 6) Deploy a Sentiment Analysis Model - deploy a PyTorch sentiment analysis model (Project: Deploying a Sentiment Analysis Model).

Enroll today (teams & execs welcome): https://tinyurl.com/2p8n3na6 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy