Resources

Nebius and NVIDIA are running the 2026 Physical AI Awards for startups with products in the field. Five category winners each get $150,000 in compute credits, joint promotion, executive mentorship, and seats at an executive dinner. Applications close October 25.

In this Folium tutorial, we build a complete set of interactive maps that run in Colab or any local Python setup. We explore multiple...

Table of contentsStep-by-Step: Mounting Google Drive in Colab (2025)Additional Tips & Updates (as of August 2025)GPU & TPU Support (2025)Summary Google Colaboratory (Colab) remains one...

Challenges of LLMs in Medical Decision-Making: Addressing Hallucinations via Knowledge Retrieval LLMs are set to revolutionize healthcare through intelligent decision support and adaptable chat-based assistants....

As autonomous AI agents move from theory into implementation, their impact on the financial services sector is becoming tangible. A recent whitepaper from IBM...

Astronomical research has transformed dramatically, evolving from limited observational capabilities to sophisticated data collection systems that capture cosmic phenomena with unprecedented precision. Modern telescopes...

The development of machine learning (ML) models for scientific applications has long been hindered by the lack of suitable datasets that capture the complexity...

Instruction-tuned large language models (LLMs) have redefined natural language processing (NLP), offering significant improvements in generating coherent, context-aware responses. However, a pressing challenge persists—access...

One of the biggest hurdles organizations face is implementing Large Language Models (LLMs) to handle intricate workflows effectively. Issues of speed, flexibility, and scalability...

Language models have made significant strides in mathematical reasoning, with synthetic data playing a crucial role in their development. However, the field faces significant...

Retrieval-augmented generation (RAG) has been a transformative approach in natural language processing, combining retrieval mechanisms with generative models to enhance factual accuracy and reasoning...

Governments and humanitarian organizations need reliable data on building and infrastructure changes over time to manage urbanization, allocate resources, and respond to crises. However,...

LAION, a prominent non-profit organization dedicated to advancing machine learning research by developing open and transparent datasets, has recently released Re-LAION 5B. This updated...

Large Language Models (LLMs) have become increasingly vital in artificial intelligence, particularly in tasks requiring no prior specific training data, known as Zero-Shot Learning....

The growth of low-quality data on the internet leads to the instillation of undesirable, unsafe, or toxic knowledge in large language models (LLMs). When...

Rightsify's Global Copyright Exchange (GCX) offers vast collections of copyright-cleared music datasets tailored for machine learning and generative AI music initiatives.  These datasets encompass...

The first extensive text-to-image prompt dataset is called DiffusionDB. It has 2 million Stable Diffusion-generated photos that were produced using prompts and hyperparameters provided...

Deep learning is used in many recent developments in computer vision and robotics; however, training deep learning models takes a lot of data to...

This Article is written as a summary by Marktechpost Staff based on the research article 'Intel Labs Accelerates Single-cell RNA-Seq Analysis'. All Credit For...

Stanford is looking to democratize research on artificial intelligence and medicine by releasing the world's largest free repository of AI-ready annotated medical imaging datasets....

Recently, Facebook released the Image Similarity data set and announced an associated competition hosted by DrivenData with a whopping $200,000 prize pool. The competition began on...

The Second Machine Age: Work, Progress and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson and Andrew McAfee In recent years, Google’s autonomous cars have logged thousands...

Hyperparameter optimization is an integral part of deep learning as a machine learning project is crucially dependent on the choice of good hyperparameters. Neural...

Object localization means locating an instance of a particular moving object in a scene. This is done by using visual data as the input...

For cutting-edge AI researchers looking for clean semantics models to represent the context-specific causal dependencies essential for causal induction, this DeepMind's algorithm encourages you to look at...

What are Hyperspectral images? Hyperspectral images (HSIs) are a kind of optical remote sensing image with a high spectral resolution. Hyperspectral images (HSIs) have attracted...

Graph Transformer Networks (GTN) is an open-source framework with weighted finite-state transducers (WFSTs), a powerful and expressive type of graph. GTN, just like PyTorch,...

Whenever any data scientist thinks of developing a pipeline, they try bringing automated machine learning into the picture to make the task easier. However,...

When we think of machine learning, the first step is to acquire and train a large dataset. However, many times the data isn’t available...

This python library 'Libra' automates the end-to-end machine learning process with just one line of code. Libra is built for both software developers and...

Totally, open-source, and made by the same development team that created the popular DVC (data version control) library, CML (Continous Machine Learning) library is a great...

ABBYY recently announced the launch of NeoML, an open-source library for building, training, and deploying machine learning models. NeoML is a cross-platform framework optimized for...

Uber Advanced Technologies Group (ATG) recently open-sourced Neuropod, a framework that provides a uniform interface to run deep learning models from multiple frameworks in...

This is really a cool work out of Microsoft research called hummingbird. You can convert traditional machine learning models to tensor computations to take...

Google AI team recently open-source BiT (Big Transfer) for general visual representation learning. Current computer vision training generally involves a pre-trained model due to...

If you are a computer scientist specialized in bioinformatics and computational biology and looking for a Python framework for simulating spiking neural networks (SNNs)...

'Horovod' is an open-source distributed deep learning framework created by Uber's AI team. This framework is used for applications in TensorFlow, Keras, PyTorch, and...

Huawei Technologies Co. Ltd. recently open-sourced 'Mindspore', a framework for artificial intelligence-based application development. This lightweight framework is ready to give competition to Google’s...

If you are looking for a tool that can perform automated visualization, AutoViz could be good one to try. It performs automatic visualization with...

In Reinforcement Learning (RL), it has always been challenging to learn from visual observations, which is a fundamental yet challenging problem. Despite algorithmic advancements...

When we talk about chatbots, good conversation is the primary object of any machine learning model and requires appropriate training data and choice of...

Human speech can be broken into four important components: content, timbre, pitch, and rhythm. The first component 'content' ofspeech shows the primary information in...

NVIDIA open sources MONAI (Medical Open Network for AI), a framework developed by NVIDIA and King’s College London for healthcare professionals using best practices from existing tools,...

If you are looking for a Python library to train and deploy supervised and unsupervised machine learning models in a low-code environment, then you...

Here is a list of COVID-19 tools and public datasets which could be really helpful in understanding the disease (COVID-19) and performing data driven...

Google open-sources FUSS data set (the Free Universal Sound Separation data set) to train and benchmark AI sound separation models. FUSS can help in...

The global novel Coronavirus (COVID-19) pandemic is rapidly evolving and expanding. There is still a lot of news coverage about community spread every day. While...

Uber Introduces Fiber, a Python-based distributed computing library for modern computer clusters. Now you can code/program your computer cluster instead of programming your desktop...

The goal of this project is to explore popular discourse about the COVID-19 pandemic and policies implemented to manage it. The author explains how...

Google has been always at the forefront when it comes to artificial intelligence research. Google AI team has recently open-sourced 'EfficientDet', an advanced object...

In this paper, the author presents softmax splatting for differentiable forward warping and demonstrated its effectiveness on the application of frame interpolation. The author...

Now you can monitor your deep learning model using an application called 'TensorDash' remotely instead of sitting in front of your workstation to monitor...

While the possibility of autonomous vehicles driving our streets in the near future is getting closer, the research in developing technologies to improve lives...

Many times when we see a video on mobile devices is badly cropped, it is not much you can do about it. Understanding this...

Yoshua Bengio: Yoshua Bengio OCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. He was a co-recipient of the 2018 ACM...

Werner Vogels: Werner Hans Peter Vogels is the chief technology officer and vice president of Amazon in charge of driving technology innovation within the company....

This paper/repo presents an unsupervised approach that enables a user to convert the input speech of a person to an output set of potentially-infinitely...

If you are looking for a tool to deploy machine learning models as production web services, then ‘Cortex’ could be a good option to try. This...

Recurrent Neural Networks(RNN) are a type of Neural Network where the output from the previous step is fed as input to the current step. In the case of Convolution Neural Networks (CNN), the output from the softmax layer in the context of image classification is entirely independent of the previous input image.

Artificial Intelligence has vast-ranging attention and its utilization in the healthcare business or industry. As an intense learner and a Kaggle beginner, I chose...

PyTorch is an Artificial Intelligence library that has been created by Facebook's artificial intelligence research group . The source code is accessible on GitHub...

Microsoft re-released an open-sourced version of its data visualization tool SandDance. This version is developed from scratch to be modular, extensible, and embeddable into your...

Netflix just announced the open-source launch of Polynote, a new Polyglot notebook type that supports multiple languages, including Python, with Apache Spark integration, first-class Scala support, as-you-type autocomplete,...

DeepFakes are AI-synthesized face-swapping videos which have currently become an emerging problem. A lot of AI labs and researchers are working on algorithms to...

Intro In this part of the series, we will introduce Recurrent Neural Networks aka RNNs that made a major breakthrough in predictive analytics for sequential...

As the leading cloud provider, Amazon Web Services offers numerous tools for a variety of applications. The sheer number of offerings can be overwhelming...

Intro Congratulations for going far in this Keras tutorial. After working with numeric, categorical and image data, it is time to handle textual data. Deep...

Neural Structured Learning (NSL) is a new open source tool to train neural networks by using Neural Graph Learning, utilizing structured data along with...

A newer version of TF-GAN (a lightweight library for training and evaluating Generative Adversarial Networks) was announced recently. TF-GAN has been used in...

SuperDataScience Learning Machines 101 IBM Analytics Insights Talking Machines Data Skeptic The O’Reilly Data Show Artificial Intelligence in Industry This Week in Machine Learning & AI Concerning AI Data Stories Partially Derivative Freakonomics...

In this blog post, we will discuss how to build a Convolution Neural Network that can classify Fashion MNIST data using Pytorch on Google Colaboratory. The way we do that is, first we will download the data using Pytorch DataLoader class and then we will use LeNet-5 architecture to build our model. Finally, we will train our model on GPU and evaluate it on the test data.

Reinforcement Learning Offered at Georgia Tech as CS 8803Practical Reinforcement LearningReinforcement Learning ExplainedReinforcement Learning in FinanceIntroduction to reinforcement learningDeep Reinforcement Learning CS 294-112 at UC BerkeleyAn...

In this blog post, we will discuss how to perform exploratory data analysis by creating awesome visualizations using matplotlib and seaborn by taking a real-world data set.

Motivation In the previous articles, we solved problems with numeric and categorical data, and we learned the different transformations needed for each. Regarding images, we...

Classification Introduction Welcome to Part 3 of Deep Learning with Keras. The goal of this series is to get you familiar with the famous deep learning...

Link to Part 1. Dealing with Data Motivation Training deep learning models requires data... A lot of data! Unfortunately, in most cases data comes messy,...

About this series This post is the first part of Deep Learning with Keras series. This series aims to introduce the Keras deep learning library and...

Here is the list of selected Data Science and Machine Learning GitHub Repositories to Try in 2019 Paper with Code Facebook’s Detectron Training a Model on the...

1. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one...

We have listed below some of the best datasets/ projects for data science beginners. 1. Enron Emails:  2. The United States Census Data:  3. Walmart:  5. CDC Cause...

Over the last few years, the data science domain has evolved exponentially. In present times, it has become the backbone of many organizations across...

Generative Adversarial Networks were first introduced in 2014 in a research paper. They have also been called  “the most interesting idea in the last...

Principal component analysis (PCA) is a statistical analysis technique that uses an orthogonal transformation to convert a set of observations of possibly correlated variables...

Here is the list of Facebook groups to join for Big Data/ Data Science International Data Science Study GroupData Science WorldArtificial Intelligence | Data Science...

Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that clusters similar data points into groups called clusters. The endpoint is a...

Reinforcement Learning is a machine learning framework that enables an agent to evaluate the current environment, take optimal action, and get feedback from the...

A human brain is not capable of solving complex data and cannot extract information from compound structures. To overcome this lack of ability to...

Reinforcement learning is a specialized area of artificial intelligence where an algorithm applies independent and goal-oriented learning, based on rewards received from an environment....

Here is a list of some of the important machine learning APIs for you to use. PREDICTION BASED MLJAR  Anaconda  Blue Yonder Platform M NuPIC  Recombee  BigML **** indico  PredictionIO **** FACE DETECTION/ RECOGNITION Betaface  Kairos API Animetrics Face...

As we did in the previous tutorial will use Gradient descent optimization algorithm. Additionally, we will divide our data set into three slices, Training,...

First Steps with TensorFlow: ToolkitGoogle + open-source = TensorFlow 9 Things You Should Know About TensorFlowDeep Learning with TensorFlow in Python TensorFlow Tutorial For BeginnersImage Segmentation...

Gradient descent is the most popular optimization algorithm, used in machine learning and deep learning. Gradient descent is iterative optimization algorithm for finding the...

We have seen tremendous progress by artificial intelligence (AI) over the past decades. This progress, however, was not achieved steadily. There were significant ups...

TensorFlow uses tensor to define the framework and processing data. A tensor conceptualized multidimensions vectors and matrices. Mathematically, a tensor is a geometric object that maps...

According to the recent study, it is evident that the artificial neural network composed of DNA has the ability to understand  numbers using molecules....

Here is the list of Facebook groups to join for Data Science learning 1.International Data Science Study Group  (~10,000 members) 2. Data Science World (~30,000 members)...

Traveling in time has always been a dream of mankind. Imagine being able to experience ancient Rome first hand or to move back to...

Below is the list of five important Data Science practice add-ins used in Microsoft Excel. 1. Power Pivot: Power Pivot add-in can be used to perform...

1.Speech and Language Processing by Dan Jurafsky and James Martin 2. Deep Learning for Natural Language Processing by Richard Socher (Stanford University) 3. Natural Language Processing (NLP)...

Machine learning and in particular deep learning revolutionize the world as we know it today. We have seen tremendous advances in speech and image...