Generative AI for Business Leaders
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How Google does Machine Learning
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Encoder-Decoder Architecture - 繁體中文
本課程概要說明解碼器與編碼器的架構，這種強大且常見的機器學習架構適用於序列對序列的任務，例如機器翻譯、文字摘要和回答問題。您將認識編碼器與解碼器架構的主要元件，並瞭解如何訓練及提供這些模型。在對應的研究室逐步操作說明中，您將學習如何從頭開始使用 TensorFlow 寫程式，導入簡單的編碼器與解碼器架構來產生詩詞。
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Course by
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Self Paced
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الإنجليزية
Using Tensorflow for Image Style Transfer
Have you ever wished you could paint like Van Gogh, Monet or even Picasso? Better yet, have you wished for an easy way to convert your own images into new ones incorporating the style of these famous artists? With Neural Style Transfer, Convolutional Neural Networks (CNNs) distill the essence of the style of any famous artist it is fed, and are able to transfer that style to any other image. In this project-based course, you will learn how to utilize Python and Tensorflow to build a Neural Style Transfer (NST) model using a VGG19 CNN.
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Course by
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Self Paced
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4 ساعات
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الإنجليزية
Visualizing Filters of a CNN using TensorFlow
In this short, 1 hour long guided project, we will use a Convolutional Neural Network - the popular VGG16 model, and we will visualize various filters from different layers of the CNN. We will do this by using gradient ascent to visualize images that maximally activate specific filters from different layers of the model. We will be using TensorFlow as our machine learning framework.
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Course by
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Self Paced
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1 ساعات
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الإنجليزية
Classification of COVID19 using Chest X-ray Images in Keras
In this 1 hour long project-based course, you will learn to build and train a convolutional neural network in Keras with TensorFlow as backend from scratch to classify patients as infected with COVID or not using their chest x-ray images. Our goal is to create an image classifier with Tensorflow by implementing a CNN to differentiate between chest x rays images with a COVID 19 infections versus without. The dataset contains the lungs X-ray images of both groups.We will be carrying out the entire project on the Google Colab environment.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Custom and Distributed Training with TensorFlow
In this course, you will: • Learn about Tensor objects, the fundamental building blocks of TensorFlow, understand the difference between the eager and graph modes in TensorFlow, and learn how to use a TensorFlow tool to calculate gradients. • Build your own custom training loops using GradientTape and TensorFlow Datasets to gain more flexibility and visibility with your model training.
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Course by
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Self Paced
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25 ساعات
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الإنجليزية
Neural Style Transfer with TensorFlow
In this 2-hour long project-based course, you will learn the basics of Neural Style Transfer with TensorFlow. Neural Style Transfer is a technique to apply stylistic features of a Style image onto a Content image while retaining the Content's overall structure and complex features. We will see how to create content and style models, compute content and style costs and ultimately run a training loop to optimize a proposed image which retains content features while imparting stylistic features from another image.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Orchestrating a TFX Pipeline with Airflow
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you'll learn to create your own machine learning pipelines using TensorFlow Extended (TFX) and Apache Airflow as the orchestrator.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Hyperparameter Tuning with Neural Network Intelligence
In this 2-hour long guided project, we will learn the basics of using Microsoft's Neural Network Intelligence (NNI) toolkit and will use it to run a Hyperparameter tuning experiment on a Neural Network. NNI is an open source, AutoML toolkit created by Microsoft which can help machine learning practitioners automate Feature engineering, Hyperparameter tuning, Neural Architecture search and Model compression. In this guided project, we are going to take a look at using NNI to perform hyperparameter tuning.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
DeepLearning.AI TensorFlow Developer
TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects.
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Course by
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Self Paced
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الإنجليزية
TensorFlow 2 for Deep Learning
This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow. The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models. The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning mode
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Course by
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Self Paced
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الإنجليزية
Create Machine Learning Models in Microsoft Azure
Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models. This course is designed to prepare you for roles that include planning and creating a suitable working environment for data science workloads on Azure.
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Course by
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Self Paced
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13 ساعات
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الإنجليزية
Building Deep Learning Models with TensorFlow
The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Natural Language Processing
Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language. This technology is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data.
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Course by
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Self Paced
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الإنجليزية
Fake Instagram Profile Detector
In this hands-on project, we will build and train a simple artificial neural network model to detect spam/fake Instagram accounts. Fake and spam accounts are a major problem in social media. Many social media influencers use fake Instagram accounts to create an illusion of having so many social media followers. Fake accounts can be used to impersonate or catfish other people and be used to sell fake services/products.
By the end of this project, you will be able to:
- Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry
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Course by
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Self Paced
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4 ساعات
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الإنجليزية
Build Multilayer Perceptron Models with Keras
In this 45-minute long project-based course, you will build and train a multilayer perceptronl (MLP) model using Keras, with Tensorflow as its backend. We will be working with the Reuters dataset, a set of short newswires and their topics, published by Reuters in 1986. It's a very simple, widely used toy dataset for text classification. There are 46 different topics, some of which are more represented than others. But each topic has at least 10 examples in the training set.
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Course by
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3 ساعات
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الإنجليزية
Deploy Models with TensorFlow Serving and Flask
In this 2-hour long project-based course, you will learn how to deploy TensorFlow models using TensorFlow Serving and Docker, and you will create a simple web application with Flask which will serve as an interface to get predictions from the served TensorFlow model.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
TensorFlow for AI: Get to Know Tensorflow
This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate offered at Coursera, which will help learners reinforce their skills and build more projects with Tensorflow.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Deep Learning with Tensorflow
Much of theworld's data is unstructured. Think images, sound, and textual data. Learn how to apply Deep Learning with TensorFlow to this type of data to solve real-world problems.
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Course by
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الإنجليزية
Creating Multi Task Models With Keras
In this 1 hour long guided project, you will learn to create and train multi-task, multi-output models with Keras. You will learn to use Keras' functional API to create a multi output model which will be trained to learn two different labels given the same input example. The model will have one input but two outputs. A few of the shallow layers will be shared between the two outputs, you will also use a ResNet style skip connection in the model. If you are familiar with Keras, you have probably come across examples of models that are trained to perform multiple tasks.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Microsoft Azure Data Scientist Associate (DP-100)
This Professional Certificate is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning s…
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Course by
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Self Paced
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الإنجليزية
TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications.
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Course by
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Self Paced
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الإنجليزية
Create Custom Layers in Keras
In this 1-hour long project-based course, you will learn how to create a custom layer in Keras, and create a model using the custom layer. In this project, we will create a simplified version of a Parametric ReLU layer, and use it in a neural network model. Then we will use the neural network to solve a multi-class classification problem. We will also compare our activation layer with the more commonly used ReLU activation layer.
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Course by
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2 ساعات
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الإنجليزية
Build a Deep Learning Based Image Classifier with R
In this 45-min guided project, you will learn the basics of using the Keras interface to R with Tensorflow as its backend to solve an image classification problem. By the time you complete this project, you will have used the R programming language to build, train, and evaluate a neural network model to classify images of clothing items into categories such as t-shirts, trousers, and sneakers. We will be training the deep learning based image classification model on the Fashion MNIST dataset which contains 70000 grayscale images of clothes across 10 categories.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Transfer Learning for NLP with TensorFlow Hub
This is a hands-on project on transfer learning for natural language processing with TensorFlow and TF Hub.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Image Noise Reduction with Auto-encoders using TensorFlow
In this 2-hour long project-based course, you will learn the basics of image noise reduction with auto-encoders. Auto-encoding is an algorithm to help reduce dimensionality of data with the help of neural networks. It can be used for lossy data compression where the compression is dependent on the given data. This algorithm to reduce dimensionality of data as learned from the data can also be used for reducing noise in data.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية