"TensorFlow is an open-source library that helps you develop and train ML models."
"I have only good news about TensorFlow 2.0, which is about a month old. It's wonderful."
"The Sequential API lets you define a stack of layers. And this is by far the most common way to build your models."
"TensorFlow takes all the details of a distributed system and just hides them from you."
"Which is why studying TensorFlow is sort of almost necessary in order to make progress in deep learning, just because it can facilitate your research and your projects."
"Behind the scenes in TensorFlow it's going to transverse backwards in your graph and compute all of those operations."
"So it's great to have such a large list of companies that are actually making use of TensorFlow as well."
"Keras is a very beautiful, nice API that sits on top of TensorFlow."
"If you can take any kind of problem you have and convert it to this function on a tensor, which is a function on an array, if you can do that you can do it in tensor flow."
"Experiment very fast with TensorFlow Serving in a production environment."
"Server Bowls are the central abstraction in TensorFlow Serving, representing fully trained models."
"Anyway, TensorFlow 2.0 is currently in alpha. It is all about ease of use, which is the number one thing I care about."
"TensorFlow Hub gives you a repository of over 10,000 pre-trained models that you can reuse and build on."
"TensorFlow API includes a full implementation of Keras."
"TensorFlow makes it really easy to define these models."
"With TensorFlow, you can actually do regression, but with a neural net."
"The good bit, the juicy bit, installing TensorFlow for deep learning."
"TensorFlow 2.0 does such a nice job that you can spin different setups up very easily and test them out."
"Google's TensorFlow is currently the most popular deep learning library in the world."
"Now we get our first line of TensorFlow code, which is exciting."
"TensorFlow 2.0 supports eager execution by default. It allows you to build your models and run them instantly."
"Cross is the official high level API of TensorFlow 2.0."
"TensorFlow 2.0 has incorporated Keras as tf.keras."
"TensorFlow 2.0 does a really good job of making it automatic."
"And that means you can write Keras code in exactly the same way you would in standard Keras inside TensorFlow."
"In my opinion, this is the easiest way to write TensorFlow code for sure when you're getting started."
"Tensorflow add-ons are little functionalities in TensorFlow that haven't been completely integrated into core TensorFlow and are mostly community contributions."
"TensorFlow is a library for developing deep learning applications, especially using neural networks."
"TensorFlow consists primarily of two parts: tensors and graphs, which handle data flow and execution."
"TensorFlow supports optimization techniques like gradient descent, where the learning rate needs to be specified for convergence."
"What is TensorFlow? TensorFlow is a popular open-source library released in 2015 by Google Brain team for building machine learning and deep learning models."
"TensorFlow has a large community and provides TensorBoard to visualize the model."
"I am really excited about this; I've been playing around with TensorFlow on the Raspberry Pi, it's a pretty cool thing to do."
"This is why TensorFlow is so predominant in a lot of areas."
"We've created an input function and again this is very specific to TensorFlow."
"Hello and welcome to this session on what is TensorFlow."
"TensorFlow is a popular open-source library released in 2015 by Google Brain team for building machine learning and deep learning models."
"This is why I love TensorFlow: I can go in there and I can build a model with different layers, each layer might have different properties."
"TensorFlow 2.0 addressed so many things out there that the 1.0 really needed."
"Keras preprocessing should be well integrated with the rest of the TensorFlow ecosystem."
"Welcome everyone, in today's video we create a speech recognition model with TensorFlow."
"You'll be able to take away these skills and use them to build deep learning models with TensorFlow."
"TensorFlow is a framework for machine learning, very complex and runs on everything."
"TensorFlow is largely meant to advance the science."
"TensorFlow is an end-to-end platform that makes it easy for you to build and deploy ML models."
"With a graph, you have a great deal of flexibility; you can use your TensorFlow graph in environments that don't have a Python interpreter."
"We have in TensorFlow an entire optimization system called Grappler that performs these and many other optimizations."
"From this tutorial, we're going to start working on machine learning problems, creating simple and maybe later on more complex applications using neural networks in TensorFlow."
"A promise, oh that's so nice of TensorFlow, not just to promise me something. What are you promising?"
"TensorFlow supports very general forms of computation, including stateful, conditional, iterative, and asynchronous computation from first principles."
"TensorFlow is a complicated system with many features, but we will focus on the ability to write simple numpy-like Python code."
"We've open-sourced TensorFlow because we think that'll make it easier to share research ideas."
"The community of TensorFlow users outside Google is growing, which is nice."
"In this video, I will show you how to train a custom object detection model with the TensorFlow Object Detection API and TensorFlow 2."
"TensorFlow was built from the ground up for deployment and scalable deployment."
"The principle application of TensorFlow is for deep learning."
"Karis is being promoted by Google as the preferred interface to TensorFlow."
"It's actually considerably more than that; TensorFlow is actually a very general purpose numerical computing library."
"TensorFlow is hardware independent, so a TensorFlow program can run equally well on a CPU or a GPU."
"I used TensorFlow for this, now I want to implement the same method from scratch in Python."
"We have this giant collection of digits... our goal today is to build a neural network using TensorFlow that can recognize these digits."
"Now you have a TensorFlow Lite model that can detect objects."
"Tensorflow... it's like batteries and lunch box included."
"With Auto ML Vision Edge, you can now build and train custom TensorFlow models to classify images with no ML or data science expertise needed."
"The architecture of TensorFlow... on the left hand side are all the things that you use for building models... on the right hand side is deployment."
"If you want to build your own, TensorFlow is your friend."
"TensorFlow is our API for building machine learning and for running machine learning models."
"TensorFlow is a deep learning library which is open source and it is pretty state of the art."
"If you're working on TensorFlow and you're using functions to do something, or you're curious about function internals, I hope this points you to the right places."
"If you have global state that you want TensorFlow functions to depend on, represent that state as a TensorFlow variable."
"Once you have your TF.function, you can call it, take its gradient, run it on the GPU, TPU, CPU, or distributed things, just like any other TensorFlow operation."
"I think we've changed a lot in TF2, how we build graphs, how we use those graphs, and I hope you'll agree with me that those changes are worth it."
"We will take up a use case implementation using TensorFlow."
"With TF.Session as sess, you don't have to explicitly close the session, the moment this with block gets completed, the session gets closed."
"As you become familiar with TensorFlow programming, I would recommend all of you to get into this model."
"That's the structure of TensorFlow program, okay, so now that we understood the structure of TensorFlow programming."
"What is the TensorFlow Object Detection API? It is an open source framework provided by the TensorFlow team."
"TensorFlow Lite allows you to run TensorFlow on Android, iOS, or Raspberry Pi."
"TensorFlow JS is for browser and node server."
"What is TensorFlow? Today we are going to see what is deep learning very briefly."
"TensorFlow is an open source library developed by Google and primarily for deep learning development."
"Using TensorFlow actually makes it much easier to write the code for GPUs or CPUs and then execute it in a distributed manner."
"Autograph feature of tf.function helps to write graph code using natural python syntax."
"We're really trying to lift TensorFlow to entirely new heights."
"TensorFlow is really well documented; there's a lot of examples out there."
"The traditional way to look at the performance of TensorFlow would be a timeline."
"If you want to be able to detect just about anything using TensorFlow object detection, this is the course for you."
"TensorBoard is an interactive monitoring tool that comes well that is open source through TensorFlow."
"That is a full end-to-end walkthrough of how to work with the TensorFlow Object Detection API."
"What's up boys and girls? Are you using tensorflow for your deep learning project?"
"TensorFlow has this built-in graph optimizations stack called Grappler, and Grappler may change the placement or it may prune away the op."
"Next time you get an error message in TensorFlow, be patient and read through the error message."
"TensorFlow is the low level mathematical library that gives you access to CPU, GPU, and grid computing capabilities."
"TensorFlow is supported by Google, has excellent support in Google Cloud, it has great CPU and GPU support and it's in Python."
"This device has a couple of microphones on it and it is using TensorFlow, the AI product from Google, to actually be able to interpret voice commands."
"Keras is now completely integrated within the TensorFlow API; it's no longer a standalone API of its own."
"TensorFlow is a free and open-source library that makes implementing various machine learning algorithms much easier."
"TensorFlow is particularly well-suited for building complex deep learning models."
"Running TensorFlow on a portable device like the Raspberry Pi provides a lot of flexibility and opportunity for cool machine learning applications."
"TensorFlow has always been extremely flexible with the goal of being able to solve any sort of problem that you throw at it."
"TensorFlow is so efficient at working with those huge networks because it turns the code that you write into a graph of operations."
"TensorFlow is real, goes into production, it was designed to go into production very easily."
"Then for building our image data generators and the model itself, we'll be using TensorFlow."
"TensorFlow is the most famous library used in the production for deep learning models."