Learn Keras Programming vs ETH Video

Keras is relatively easy to learn and work with because it provides a python frontend with a high level of abstraction while having the option of multiple back-ends for computation purposes. This makes Keras slower than other deep learning frameworks, but extremely beginner-friendly. Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. You can export Keras models to JavaScript to run directly in the browser It's also easy to serve Keras models as via a web API. Keras is a central part of the tightly-connected TensorFlow 2 ecosystem, covering every step of the machine learning workflow, from data management to hyperparameter training to deployment solutions. Keras is used by CERN, NASA, NIH, and many more scientific organizations around the world (and yes, Keras is used at the LHC). Keras has the low-level flexibility to implement arbitrary research ideas while offering optional high-level convenience features to speed up experimentation cycles. Because of its ease-of-use and focus on user experience, Keras is the deep learning solution of choice for many university courses. It is widely recommended as one of the best ways to learn deep learning. Keras API reference Models API The Model class The Sequential class Model training APIs Model saving & serialization APIs Layers API The base Layer class Layer activations Layer weight initializers Layer weight regularizers Layer weight constraints Core layers Convolution layers Pooling layers Recurrent layers Preprocessing layers Normalization layers Regularization layers Attention layers Reshaping layers Merging layers Locally-connected layers Activation layers Callbacks API Base Callback class ModelCheckpoint BackupAndRestore TensorBoard EarlyStopping LearningRateScheduler ReduceLROnPlateau RemoteMonitor LambdaCallback TerminateOnNaN CSVLogger ProgbarLogger

ETH Video lets you catch up on ETH Zürich lectures anywhere: stream or download the recordings, search their transcripts and watch at your own speed. Add each lecture series once, and it stays in one list on iPhone, iPad, Mac and Apple Vision Pro. • Offline downloads — save recordings in the quality you choose and watch them on the train • Searchable transcripts — where ETH provides one, find where a topic comes up and jump straight to it • Your own pace — play at 0.5x to 2.5x, or keep watching in Picture in Picture while you take notes • Watch History — turn it on to resume where you stopped and see how many recordings are left • Restricted lectures — watch login-only recordings with your ETH Account or a lecture's own password • SharePlay — watch with classmates on FaceTime, with playback kept in sync • iCloud sync — optionally keep your lecture list, settings and Watch History on all your devices ETH Video is free, with no sign-up, tracking or analytics, and your ETH sign-in is stored securely in your Keychain. It is an independent app, not affiliated with ETH Zürich; recordings and transcripts come from ETH. Privacy Policy: https://ethvideo.ch/privacy Terms of Use: https://ethvideo.ch/terms