TensorFlow: A Guide For IT Pros

TensorFlow is an open source software library developed by Google for numerical computation with data flow graphs. It offers tremendous opportunities for developers building machine learning into their products. This ebook looks at what TensorFlow is, where it’s headed, and how it’s being put to work.
From the ebook:

What is TensorFlow?When you have a photo of the Eiffel Tower, Google Photos can identify the image. This is possible thanks to machine learning and developments like TensorFlow. Prior to TensorFlow there was a division between the researchers of machine learning and those developing real products; that division made it challenging for developers to include machine learning in their software. With TensorFlow, that division is gone.

TensorFlow delivers a set of modules (providing for both Python and C/C++ APIs) that enable constructing and executing TensorFlow computations, which are then expressed in stateful data flow graphs. These graphs make it possible for applications like Google Photos to become incredibly accurate at recognizing locations in images based on popular landmarks.

In 2011, Google developed a product called DistBelief that worked on the positive reinforcement model. The machine would be given a picture of a cat and asked if it was a picture of a cat. If the machine guessed correctly, it was told so. An incorrect guess would lead to an adjustment so that it could better recognize the image.

TensorFlow improves on this concept by sorting through layers of data called Nodes. Diving deeper into the layers allows for additional and more complex questions about an image. For example, a first-layer question might simply require the machine to recognize a round shape. In deeper layers, the machine might be asked to recognize a cat’s eye. The flow process (from input, through the layers of data, to output) is called a tensor … Hence the name TensorFlow.

Tensorflow Tutorial

#tensorflow #keras

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5 Steps to Passing the TensorFlow Developer Certificate

Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework. One of the best ways in my opinion to show that you are comfortable with DL fundaments is taking this TensorFlow Developer Certificate. I completed mine last week and now I am giving tips to those who want to validate your DL skills and I hope you love Memes!

1. Do the DeepLearning.AI TensorFlow Developer Professional Certificate Course on Coursera Laurence Moroney and by Andrew Ng.

2. Do the course questions in parallel in PyCharm.

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#tensorflow #steps to passing the tensorflow developer certificate #tensorflow developer certificate #certificate #5 steps to passing the tensorflow developer certificate #passing

Beginners Guide to Debugging TensorFlow Models

If you are new to working with a deep learning framework, such as TensorFlow, there are a variety of typical errors beginners face when building and training models. Here, we explore and solve some of the most common errors to help you develop a better intuition for debugging in TensorFlow.

TensorFlow is one of the most famous deep learning models, and it is easy to learn. This article will discuss the most common errors a beginner can face while learning TensorFlow, the reasons, and how to solve these errors. We will discuss the solutions and also what experts from StackOverflow say about them.

Example 1: Wrong Input Shape for CNN layer

Example 2: Negative Dimension Size

Example ## 3: Wrong Output Shape

Example ## 4: Unknown Loss function

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#2021 jun tutorials #overviews #beginners #deep learning #tensorflow #beginners guide to debugging tensorflow models

TensorFlow: A Guide For IT Pros

TensorFlow is an open source software library developed by Google for numerical computation with data flow graphs. It offers tremendous opportunities for developers building machine learning into their products. This ebook looks at what TensorFlow is, where it’s headed, and how it’s being put to work.
From the ebook:

What is TensorFlow?When you have a photo of the Eiffel Tower, Google Photos can identify the image. This is possible thanks to machine learning and developments like TensorFlow. Prior to TensorFlow there was a division between the researchers of machine learning and those developing real products; that division made it challenging for developers to include machine learning in their software. With TensorFlow, that division is gone.

TensorFlow delivers a set of modules (providing for both Python and C/C++ APIs) that enable constructing and executing TensorFlow computations, which are then expressed in stateful data flow graphs. These graphs make it possible for applications like Google Photos to become incredibly accurate at recognizing locations in images based on popular landmarks.

In 2011, Google developed a product called DistBelief that worked on the positive reinforcement model. The machine would be given a picture of a cat and asked if it was a picture of a cat. If the machine guessed correctly, it was told so. An incorrect guess would lead to an adjustment so that it could better recognize the image.

TensorFlow improves on this concept by sorting through layers of data called Nodes. Diving deeper into the layers allows for additional and more complex questions about an image. For example, a first-layer question might simply require the machine to recognize a round shape. In deeper layers, the machine might be asked to recognize a cat’s eye. The flow process (from input, through the layers of data, to output) is called a tensor … Hence the name TensorFlow.

Tensorflow Tutorial

#tensorflow #keras

Transfer Learning on Images with Tensorflow 2 – Predictive Hacks

In this tutorial, we will provide you an example of how you can build a powerful neural network model to classify images of **cats **and dogs using transfer learning by considering as base model a pre-trained model trained on ImageNet and then we will train additional new layers for our cats and dogs classification model.

The Data

We will work with a sample of 600 images from the Dogs vs Cats dataset, which was used for a 2013 Kaggle competition.

#python #transfer learning #tensorflow #images #transfer learning on images with tensorflow #tensorflow 2

Converting A Model From Pytorch to Tensorflow: Guide To ONNX

Open Neural Network Exchange (ONNX) is a powerful and open format built to represent machine learning models. The final outcome of training any machine learning or deep learning algorithm is a model file that represents the mapping of input data to output predictions in an efficient manner. These models are stored in different file formats depending on the framework they were created in .pkl for Scikit-learn, .pb for TensorFlow, .pth for PyTorch, and so on. Therein lies the problem, you can’t take a model created and trained in one framework and use it or deploy it in a different framework.

The intent behind ONNX is to be like the “USB standard” of the machine learning world. Before the introduction of USB computers and computer peripherals used to have ad hoc proprietary interfaces. Much like the pre-USB era, the present machine learning models have ad hoc formats. It overcomes the problem of framework lock-in by providing a universal intermediary model format that frameworks can easily save to and load from. This allows ML developers to create models in the framework of their choice without worrying about the deployment environment. ONNX also makes it easier to access hardware acceleration provided by different frameworks and runtime environments.

#developers corner #pytorch to tensorflow #tensorflow