Intro to Deep Learning (ML Intensive at X)

An overview of Deep Learning, including representation learning, families of neural networks and their applications, a first look inside a deep neural network, and many code examples and concepts from TensorFlow. This talk is part of a ML speaker series at X we recorded at home. You can find all the links from this video below. I hope this was helpful, and I'm looking forward to seeing you when we can get back to doing events in person. Thanks everyone! Chapters: 0:00 - Intro and outline 1:42 - TensorFlow.js demos + discussion 3:58 - AI vs ML vs DL 7:55 - What’s representation learning? 8:40 - A cartoon neural network (more on this later) 9:20 - What features does a network see? 10:47 - The “deep” in “deep learning” 12:48 - Why tree-based models are still important 13:38 - How your workflow changes with DL 14:02 - A couple illustrative code examples 17:59 - What’s a hyperparameter? 19:44 - The skills that are important in ML 20:48 - An example of applied work in healthcare 21:58 - Families of neural networks + applications 28:55 - Encoder-decoders + more on representation learning 32:45 - Families of neural networks continued 35:50 - Are neural networks opaque? 38:29 - Building up from a neuron to a neural network 49:11 - A demo of representation learning in TF Playground 53:24 - Importance of activation functions 54:36 - What’s a neural network library? 58:43 - Overfitting and underfitting 1:02:38 - Autoencoders (and anomaly detection) screencast and demo 1:12:13 - Book recommendations Here are three helpful classes you can check out to learn more: Intro to Deep Learning from MIT → http://goo.gle/3sPj8To MIT Deep Learning and Artificial Intelligence Lectures → https://goo.gle/3qh7H54 Convolutional Neural Networks for Visual Recognition from Stanford → http://goo.gle/3bbC34I And here are all the links to demos and code from the video, in the order they appeared: Face and hand tracking demos → http://goo.gle/2WTCwSc Teachable machine demo → https://goo.gle/3bSCzCi What features does a network see? → http://goo.gle/3e2zpA5 DeepDream tutorials → http://goo.gle/3bYIBTp and http://goo.gle/384B6JC Hyperparameter tuning with Keras Tuner → http://goo.gle/2InBK7J Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs → http://goo.gle/309pMY5 Linear (and deep) regression tutorial → http://goo.gle/3sKxkN7 Image classification with a CNN tutorial → http://goo.gle/3qdD2Wb Audio recognition tutorial → http://goo.gle/3kFpl1j Transfer learning tutorial → http://goo.gle/3bV7D60 RNN tutorial (sentiment analysis / text classification) → http://goo.gle/3bVM1X7 RNN tutorial (text generation with Shakespeare) → http://goo.gle/3qmnrnz Timeseries forecasting tutorial (weather) → http://goo.gle/3ecdYg9 Sketch RNN demo (draw together with a neural network) → http://goo.gle/3bbHTTy Machine translation tutorial (English to Spanish) → http://goo.gle/3e7IJme Image captioning tutorial → http://goo.gle/3sKFNQz Autoencoders and anomaly detection tutorial → http://goo.gle/30aD0UA GANs tutorial (Pix2Pix) → http://goo.gle/3kI1ZrB A Deep Learning Approach to Antibiotic Discovery → https://goo.gle/3e7ivQD Integrated gradients tutorial → http://goo.gle/2PxfRtq and http://goo.gle/3sE0bmq TensorFlow Playground demos → http://goo.gle/2Px6rhB Introduction to gradients and automatic differentiation → http://goo.gle/3sFVybo Basic image classification tutorial → http://goo.gle/3c2AF3o Overfitting and underfitting tutorial → http://goo.gle/3cdA9Qv Keras early stopping callback → http://goo.gle/308XQUj Interactive autoencoders demo (anomaly detection) → http://goo.gle/3kPfW7q Deep Learning with Python, Second Edition → http://goo.gle/3qcQ5Y5 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition → http://goo.gle/386DKP4 Deep Learning book → http://goo.gle/3c2VQmd Find Josh on Twitter → https://goo.gle/308Ve8P Subscribe to TensorFlow → https://goo.gle/TensorFlow