If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Enthusiasm and determination to make your mark on the world! Enthusiasm and determination to make your mark on the world! TensorFlow is an end-to-end open-source machine learning / deep learning platform. It has a comprehensive ecosystem of libraries, tools, and community resources that lets AI/ML engineers, scientists, analysts build and deploy ML-powered deep learning applications. The name TensorFlow is derived from the operations which neural networks perform on multidimensional data arrays or tensors.
In this article, we will try to implement the basic CNN model with the Keras framework. The benefit of the convolutional neural network is that it reduces or minimizes the dimension and parameters of images by retaining maximum information so that the training process becomes fast and takes less computation power. We will try to implement the code in google colab with a step-by-step process. Why we are using CNN? The main concern of using the convolutional neural network is for the images that previous algorithms are not so much suitable for bulk images dataset and retaining the image information.
This course is part of the TensorFlow: Advanced Techniques Specialization Build custom loss functions (including the contrastive loss function used in a Siamese network) in order to measure how well a model is doing and help your ... The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing.
This course is part of the DeepLearning.AI TensorFlow Developer Professional Certificate If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You'll first implement best practices to prepare time series data. You'll also explore how RNNs and 1D ConvNets can be used for prediction.
You will also learn how to use TensorFlow For NLP and Deep Learning. By end of this course, you will learn how to build a Sentiment Classifier and a program that can write like a real poet! This course is very hands-on and you will be learning everything there is about basic NLP. For those of you who like to dig deep into the theory to understand how things really work, you know this is my specialty and there will be no shortage of that in this course. We'll be covering the state of the art algorithms like word embeddings, tokenization, and deep learning.
Tensorflow/Keras & Pytorch are by far the 2 most popular major machine learning libraries. Tensorflow is maintained and released by Google while Pytorch is maintained and released by Facebook. There are multiple changes between Tensorflow 1 and Tensorflow 2.x, I am going to try to pinpoint the most important ones. The first one is the release of Tensorflow.js. With web applications being more and more dominant, the need for deploying models on browsers has grown quite a lot.
Google's TensorFlow and Facebook's PyTorch are the most popular machine learning frameworks. The former has a two-year head start over PyTorch (released in 2016). TensorFlow's popularity reportedly declined after PyTorch bursted into the scene. However, Google released a more user-friendly TensorFlow 2.0 in January 2019 to recover lost ground. PyTorch is emerging as a leader in terms of papers in leading research conferences.
It is frequent to encounter class imbalance when developing models for real-world applications. This occurs when there are substantially more instances associated with one class than with the other. For example, in a Credit Risk Modeling project, when looking at the status of loans in historical data, most of the loans being granted have probably been paid in full. If models susceptible to class imbalance are used, defaulted loans would probably not have much relevance in the training process, as the overall loss continues to decrease when the model focuses on the majority class. To make the model pay more attention to examples where the loan was defaulted, class weights can be used so that the prediction error is larger when an instance of the underrepresented class is incorrectly classified.
Editor's Note: Multi-objective optimization (MOO) is used for many products at LinkedIn (such as the homepage feed) to help balance different behaviors in our ecosystem. There are two parts to how we work with multiple objectives: the first is about training high-fidelity models to predict member behavior (e.g., probability a member will click an article). The second is around trading off different objectives for a unified member experience based on utility to the LinkedIn ecosystem (e.g., a comment is much more valuable than a click). This post will focus on the first part of multi-objective optimization, where we utilize a multi-task, deep learning model to create higher fidelity consumption models; for more information on the second part, objective tradeoffs, see this article from KDnuggets about automatically tuning this tradeoff for faster model iteration. LinkedIn's members rely on the homepage feed for a variety of content including updates from their network, industry articles, and new job opportunities.