Generative AI for Business Leaders
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How Google does Machine Learning
دوراتنا
Introduction to Convolutions with TensorFlow
This is a self-paced lab that takes place in the Google Cloud console. A convolution is a filter that passes over an image, processes it, and extracts features that show a commonality in the image. In this lab you'll see how they work, and try processing an image to extract features from it! You also explore pooling, which compresses your image and further emphasizes the features.
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Course by
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Self Paced
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1 ساعات
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الإنجليزية
Classify Images with TensorFlow Convolutional Neural Networks
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you'll learn about how to use convolutional neural networks to improve your image classification models.
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Course by
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Self Paced
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1 ساعات
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الإنجليزية
Perform data science with Azure Databricks
In this course, you will learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run data science workloads in the cloud. This is the fourth course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurec ertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning.
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Course by
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Self Paced
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26 ساعات
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الإنجليزية
Basic Sentiment Analysis with TensorFlow
Welcome to this project-based course on Basic Sentiment Analysis with TensorFlow. In this project, you will learn the basics of using Keras with TensorFlow as its backend and you will learn to use it to solve a basic sentiment analysis problem. By the end of this 2-hour long project, you will have created, trained, and evaluated a Neural Network model that, after the training, will be able to predict movie reviews as either positive or negative reviews - classifying the sentiment of the review text.
Notes:
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Course by
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Self Paced
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4 ساعات
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الإنجليزية
Learning TensorFlow: the Hello World of Machine Learning
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you learn the basic ‘Hello World' of machine learning. Instead of programming explicit rules in a language such as Java or C++, you build a system that is trained on data to infer the rules that determine a relationship between numbers.
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Course by
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Self Paced
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1 ساعات
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الإنجليزية
Simple Recurrent Neural Network with Keras
In this hands-on project, you will use Keras with TensorFlow as its backend to create a recurrent neural network model and train it to learn to perform addition of simple equations given in string format. You will learn to create synthetic data for this problem as well. By the end of this 2-hour long project, you will have created, trained, and evaluated a sequence to sequence RNN model in Keras. Computers are already pretty good at math, so this may seem like a trivial problem, but it’s not! We will give the model string data rather than numeric data to work with.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Image Super Resolution Using Autoencoders in Keras
Welcome to this 1.5 hours long hands-on project on Image Super Resolution using Autoencoders in Keras. In this project, you’re going to learn what an autoencoder is, use Keras with Tensorflow as its backend to train your own autoencoder, and use this deep learning powered autoencoder to significantly enhance the quality of images. That is, our neural network will create high-resolution images from low-res source images.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Neural Network from Scratch in TensorFlow
In this 2-hours long project-based course, you will learn how to implement a Neural Network model in TensorFlow using its core functionality (i.e. without the help of a high level API like Keras). You will also implement the gradient descent algorithm with the help of TensorFlow's automatic differentiation. While it’s easier to get started with TensorFlow with the Keras API, it’s still worth understanding how a slightly lower level implementation might work in tensorflow, and this project will give you a great starting point.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Understanding Deepfakes with Keras
In this 2-hour long project-based course, you will learn to implement DCGAN or Deep Convolutional Generative Adversarial Network, and you will train the network to generate realistic looking synthesized images. The term Deepfake is typically associated with synthetic data generated by Neural Networks which is similar to real-world, observed data - often with synthesized images, videos or audio.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Deep-Dive into Tensorflow Activation Functions
You've learned how to use Tensorflow. You've learned the important functions, how to design and implement sequential and functional models, and have completed several test projects. What's next? It's time to take a deep dive into activation functions, the essential function of every node and layer of a neural network, deciding whether to fire or not to fire, and adding an element of non-linearity (in most cases). In this 2 hour course-based project, you will join me in a deep-dive into an exhaustive list of activation functions usable in Tensorflow and other frameworks.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
TensorFlow on Google Cloud - Español
En este curso, se explica cómo crear modelos de AA con TensorFlow y Keras, cómo mejorar la exactitud de los modelos de AA y cómo escribir modelos de AA para uso escalado.
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Course by
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Self Paced
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الإنجليزية
Prepare for DP-100: Data Science on Microsoft Azure Exam
Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses. In this course, you will prepare to take the DP-100 Azure Data Scientist Associate certification exam. You will refresh your knowledge of how to plan and create a suitable working environment for data science workloads on Azure, run data experiments, and train predictive models.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
TensorFlow on Google Cloud - Português Brasileiro
Este curso ensina a criar modelos de ML com o TensorFlow e o Keras, melhorar a acurácia deles e desenvolver modelos para uso em escala.
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Course by
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Self Paced
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الإنجليزية
Machine Learning: Concepts and Applications
This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques.
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Course by
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Self Paced
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38 ساعات
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الإنجليزية
Support Vector Machines in Python, From Start to Finish
In this lesson we will built this Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. Our training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Creating Custom Callbacks in Keras
In this 1.5-hour long project-based course, you will learn to create a custom callback function in Keras and use the callback during a model training process. We will implement the callback function to perform three tasks: Write a log file during the training process, plot the training metrics in a graph during the training process, and reduce the learning rate during the training with each epoch.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
TensorFlow on Google Cloud - 한국어
이 과정에서는 TensorFlow 및 Keras를 사용한 ML 모델 빌드, ML 모델의 정확성 개선, 사용 사례 확장을 위한 ML 모델 작성에 대해 다룹니다.
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Course by
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Self Paced
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الإنجليزية
Emotion AI: Facial Key-points Detection
In this 1-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Deep Learning, Convolutional Neural Networks (CNNs) and Residual Neural Networks.
- Import Key libraries, dataset and visualize images.
- Perform data augmentation to increase the size of the dataset and improve model generalization capability.
- Build a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 2.0 as a backend.
- Compile and fit Deep Learning model to training data.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Predict Baby Weight with TensorFlow on AI Platform
In this lab you train, evaluate, and deploy a machine learning model to predict a baby’s weight. You then send requests to the model to make online predictions. This lab is part of a series of labs on processing scientific data.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
TensorFlow Serving with Docker for Model Deployment
This is a hands-on, guided project on deploying deep learning models using TensorFlow Serving with Docker. In this 1.5 hour long project, you will train and export TensorFlow models for text classification, learn how to deploy models with TF Serving and Docker in 90 seconds, and build simple gRPC and REST-based clients in Python for model inference.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Regression with Automatic Differentiation in TensorFlow
In this 1.5 hour long project-based course, you will learn about constants and variables in TensorFlow, you will learn how to use automatic differentiation, and you will apply automatic differentiation to solve a linear regression problem. By the end of this project, you will have a good understanding of how machine learning algorithms can be implemented in TensorFlow.
In order to be successful in this project, you should be familiar with Python, Gradient Descent, Linear Regression.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Advanced Computer Vision with TensorFlow
In this course, you will: a) Explore image classification, image segmentation, object localization, and object detection.
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Course by
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19 ساعات
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الإنجليزية
Interpretable machine learning applications: Part 3
In this 50 minutes long project-based course, you will learn how to apply a specific explanation technique and algorithm for predictions (classifications) being made by inherently complex machine learning models such as artificial neural networks. The explanation technique and algorithm is based on the retrieval of similar cases with those individuals for which we wish to provide explanations.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
ML Pipelines on Google Cloud
In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX.
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
Siamese Network with Triplet Loss in Keras
In this 2-hour long project-based course, you will learn how to implement a Triplet Loss function, create a Siamese Network, and train the network with the Triplet Loss function. With this training process, the network will learn to produce Embedding of different classes from a given dataset in a way that Embedding of examples from different classes will start to move away from each other in the vector space.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية