Deep Learning (Training)

Colleagues, the Deep Learning training program will equip you 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. Get your first taste of deep learning by applying style transfer to your own images, and gain experience using development tools such as Anaconda and Jupyter notebooks. Skill-based training modules include: 1) Neural Networks - build your first network with Python and NumPy. Use the modern deep learning framework PyTorch to build multi-layer neural networks, and analyze real data., 2) Convolutional Neural Networks - build and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image denoising., 3) Recurrent Neural Networks - build RNNs and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts, 4) Generative Adversarial Network - use GANs to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs, and 5) Deploying a Sentiment Analysis Model - train and deploy your own PyTorch sentiment analysis model. Deployment gives you the ability to use a trained model to analyze new user input. Build a model, deploy it, and create a gateway for accessing it from a website (Project: Deploying a Sentiment Analysis Model). Knowledge of Python, NumPy, pandas, calculus and linear algebra is recommended.

Enroll today (teams & execs welcome): https://tinyurl.com/yj8zehht 

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