Federated Learning: Google’s Take
This blog will be focusing on the work Google has been doing in the Federated Learning space. In 2017, Google AI Research published a paper on Federated Learning: Collaborative Machine Learning without Centralized Training Data. The approach stands in contrast to traditional centralized machine learning techniques where all the local datasets are uploaded to one server, as well as to more classical decentralized approaches.
Our Journey towards Data-Centric AI: A Retrospective
This article provides a brief, biased retrospective of our road to data-centric AI. Our hope is to provide an entry point for people interested in this area, which has been scattered to the nooks and crannies of AI even as it drives some of our favorite products, advancements, and benchmark improvements. We’re collecting pointers to these resources on GitHub, and plan to write a few more articles about exciting new directions.
Buy Til You Die: Predict Customer Lifetime Value in Python
The Insights We Are Looking for are based on non-contractual business settings. We want to predict the value of a customer lifetime in Python Python. We also want to use the Python language to predict customer lifetime value in Python. Python is a language that can be written in Python, Python and Python.
A Critical Look at How Data Science is Taught
Data science is particularly interesting from an educational/pedagogical standpoint. Data science education is more decentralized than traditional data science education, says John Defterios. He says data science should be taught in a decentralized way of learning. Defterio: Data science and computer science education should be decentralized. He suggests that data science is a model for the future of data science.
10 AI Project Ideas in Computer Vision
The field of computer vision has seen the development of very powerful applications leveraging machine learning. These projects will introduce you to these techniques and guide you to more advanced practice. The project ideas have been split into categories mentioned below so you can smoothly browse through them as per your experience in the industry. There are many exciting applications of Computer Vision (CV), and in this blog, we are going to list AI project ideas that a CV enthusiast can work on.
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability
Many experimental works have observed that generalization in deep RL appears to be difficult. Although RL agents can learn to perform very complex tasks, they don’t seem to generalize over diverse task distributions as well as the excellent generalization of supervised deep nets might lead us to expect. This blog post will walk through why partial observability can implicitly arise, what it means for the generalization performance of RL algorithms.
3 Differences Between Coding in Data Science and Machine Learning
The terms data science and machine learning are often used interchangeably. While they are related, there are some glaring differences, especially between the responsibilities of developers working in either field. Data scientists perform algorithmic coding, statistics, and data processing to formulate research questions, analyze the data, and present results as written and visual reports. Data science is an increasingly sought after skill set in all manner of fields, from business to computer science.
Data Visualization Before Machine Learning
Machine learning is a powerful tool that can be used in data visualizations. It’s a way of showing the power of machine learning rather than old dashboards. Do you ever ask yourself why your machine learning model isn’t used? Why do so few people really believe in machine learning? We ask you why your model isn’t used in our dashboards?
Deploy an object detector model at the edge on AWS Panorama
Computer Vision has become one of the most exciting fields of application for Deep Learning. CNN models can reach human-like accuracy levels detecting objects within an image or a video stream. These incredible advancements opened a broad field of application ranging from retail customer behavior analysis to security and industrial quality assurance. Edge computing became a desirable solution to achieve better latencies and lower the total system cost, opening an entirely new market.
What is federated learning?
The traditional process for developing machine learning applications is to gather a large dataset, train a model on the data, and run the trained model on a cloud server. In applications such as text autocompletion or facial recognition, the data is local to the user and the device. Advances in edge AI have made it possible to avoid sending sensitive user data to application servers.
Demystifying deep reinforcement learning
This article is part of a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Deep reinforcement learning leverages the learning capacity of deep neural networks to tackle problems that were too complex for classic RL techniques. It is behind some of the most remarkable achievements of the AI community, including beating human champions at board and video games, self-driving cars, robotics, and robotics.
Hands-on reinforcement learning course part 1
The agent picks the action she thinks is the best based on the current state of the environment. This is the agent’s strategy, commonly referred to as the agent’s policy. The optimal policy is the one where at each state s the agent chooses the action a that maximizes the optimal value function. Value functions can also map pairs of (action, state) to values, called q-value functions.
Catching Feels
Humans show a myriad of explicit and implicit emotional signals in our behaviors. Our facial expression, posture, and even the music we listen to are types of expressions that tell the overarching story of how we feel. Most of these signals are implicitly communicated during human-to-human interaction, we do not have a method for quantifying feeling and mood through individual behavioral signals expressed on the digital platform.
Complete Guide to Perform Classification of Tweets with SpaCy
The models I chose can be separated into two categories: statistical language models, logistic regression, and neural language models. Common probabilistic models use order-specific N-grams and orderless Bag-of-Words models (BoW) to transform the data before inputting the data into the predictor. To train the models, we use scikit-learn’s Pipeline module that groups the cleaning of data, the vectorization, and the classification into one pipeline.
AGQA: A Benchmark for Compositional, Spatio-Temporal Reasoning
People have a remarkable ability to comprehend visual events in new videos and to answer questions about that video. We can decompose visual events and actions into individual interactions between the person and other objects. Designing machines that can exhibit compositional understanding of visual events has been a core goal of the computer vision community. To measure progress towards this goal, the community has released numerous video question answering benchmarks.
SEAL link prediction explained
For each link in edge_index we need to extract the enclosed subgraph defined by the number of hops, source and target nodes, and label each node in each subgraph according to Double-Radius Node Labelling (DRNL) algorithm. The labeling method captures the structural roles of the nodes with respect to the source and the target nodes. We need to split our data into train, test and validation, create negative examples and plug them all into a Dataloader.
Comparing the Five Most Popular EDA Tools
Exploratory Data Analysis (EDA) is an integral part of any data science project. Data scientists use EDA mainly to formalize various hypotheses for testing and next steps for data engineering. We will review five of the most popular Python EDA tools: DataPrep, Pandas-profiling, SweetViz, Lux, and D-Tale.
Dask DataFrame is not Pandas
This article is the second article of an ongoing series on using Dask in practice. Each article in this series will be simple enough for beginners, but provide useful tips for real work. Dask is a great way to scale up your Pandas code. The fundamental shift should not be to replace Pandas with Dask, but to re-use the algorithms, code, and methods you wrote for a single Python process.
AI Infinite Training & Maintaining Loop
Productizing AI is an infrastructure orchestration problem. In planning your solution design, you should use continuous monitoring, retraining, and feedback to ensure stability and sustainability. By Roey Mechrez, Ph.D., CTO and co-founder of BeyondMinds, he says it’s a painful reality for many companies, especially when it comes to mission-critical or operational sensitive applications.