ODSC - Open Data Science

MARCH 12, 2025

AI engineering extended this by integrating AI systems more deeply into software engineering pipelines, making it a crucial field as AI applications became more sophisticated and embedded in real-world systems. Takeaway: The industrys focus has shifted from building models to making them robust, scalable, and maintainable.

Data Science 52

Data Science ETL Machine Learning AI Engineer 52

Mlearning.ai

JULY 17, 2023

How taking inspiration from the brain can help us create Neural Networks. One of the most promising avenues for this kind of research is to learn from evolution and biological systems to improve the design of AI Models (after all Neural Networks were based on our brains).

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Neural Network Convolutional Neural Networks Machine Learning Data Scientist 52

NVIDIA

DECEMBER 4, 2023

A RedHat software engineering team put it succinctly in a blog : “GPUs have become the foundation of artificial intelligence.” An AI model, also called a neural network, is essentially a mathematical lasagna, made from layer upon layer of linear algebra equations. The University of Toronto professor spread the word. “In

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Neural Network AI AI LLM 134

Marktechpost

APRIL 28, 2024

It covers various topics like graph search algorithms, classification, optimization, machine learning, and large language models with the help of hands-on projects, allowing the students to incorporate them into their own Python programs. Machine Learning for All This course introduces machine learning without needing any programming.

Artificial Intelligence 107

Artificial Intelligence Artificial Intelligence Prompt Engineering Prompt Engineer 107

Towards AI

AUGUST 24, 2023

Common mistakes and misconceptions about learning AI/ML Markus Spiske on Unsplash A common misconception of beginners is that they can learn AI/ML from a few tutorials that implement the latest algorithms, so I thought I would share some notes and advice on learning AI. Trying to learn AI from research papers.

Neural Network 98

Neural Network Software Engineer Algorithm Machine Learning 98

Dlabs.ai

JUNE 19, 2020

If you Google ‘ what’s needed for deep learning ,’ you’ll find plenty of advice that says vast swathes of labeled data (say, millions of images with annotated sections) are an absolute must. You may well come away thinking, deep learning is for ‘superhumans only’ — superhumans with supercomputers. Sounds interesting?

Deep Learning 52

Deep Learning Neural Network Machine Learning Computer Vision 52

Towards AI

AUGUST 25, 2023

What is AI Engineering AI Engineering is a new discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts [1]. In a nutshell, AI Engineering is the application of software engineering best practices to the field of AI. Bourque and R.

Machine Learning 98

Machine Learning Software Engineer Neural Network Data Science 98

IBM Journey to AI blog

JULY 6, 2023

Python is the most common programming language used in machine learning. Machine learning and deep learning are both subsets of AI. Deep learning teaches computers to process data the way the human brain does. Deep learning algorithms are neural networks modeled after the human brain.

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Data Science Machine Learning Big Data Data Mining 175

Mlearning.ai

JULY 20, 2023

In case you missed it, make sure you catch part 1 of this series- A Neuroscientists view into Improving Neural Networks - where we talked about the biological basis of bilaterality and how the asymmetric nature of our brains leads to greater performance. If any of you want to work on this, you know how to reach me.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Software Engineer 52

Flipboard

JULY 31, 2023

Object detection works by using machine learning or deep learning models that learn from many examples of images with objects and their labels. In the early days of machine learning, this was often done manually, with researchers defining features (e.g., Object detection is useful for many applications (e.g.,

Machine Learning 123

Machine Learning Computer Vision Deep Learning Convolutional Neural Networks 123

ODSC - Open Data Science

MARCH 28, 2023

Learn more about this brand new track here ! Machine Learning and Deep Learning This track gathers together the creators and top practitioners in the rapidly expanding fields of deep learning and machine learning to discuss the latest advances, trends, and models in these fields.

Data Science 92

Data Science Deep Learning Machine Learning Neural Network 92

AWS Machine Learning Blog

MARCH 13, 2025

MoE models like DeepSeek-V3 and Mixtral replace the standard feed-forward neural network in transformers with a set of parallel sub-networks called experts. He helps customers build, train, deploy, evaluate, and monitor Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI) workloads on Amazon SageMaker.

LLM 83

LLM Machine Learning AI AI 83

How to Learn Machine Learning

DECEMBER 15, 2024

In the rapidly evolving world of technology, machine learning has become an essential skill for aspiring data scientists, software engineers, and tech professionals. Coursera Machine Learning Courses are an exceptional array of courses that can transform your career and technical expertise.

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Software Engineer 52

ODSC - Open Data Science

MARCH 13, 2023

They are experts in machine learning, NLP, deep learning, data engineering, MLOps, and data visualization. Learn more about a few of our ODSC East 2023 instructors, their backgrounds in education, and why they’re fit for imparting their knowledge. Dr. Jon Krohn Chief Data Scientist | Nebula.io

Data Science 80

Data Science Deep Learning Machine Learning Software Engineer 80

Dlabs.ai

DECEMBER 9, 2021

Deep Learning for NLP and Speech Recognition Authors : Uday Kamath , John Liu , James Whitaker This book looks at applying deep learning architecture to tasks like document classification, translation, language modeling, and speech recognition.

Natural Language Processing 59

Natural Language Processing Neural Network NLP Deep Learning 59

ODSC - Open Data Science

MARCH 27, 2023

Scaling AI/ML Workloads with Ray Kai Fricke | Senior Software Engineer | Anyscale Inc. To solve the challenges that make production machine learning systems difficult to use, the Ray community has built Ray AI Runtime (Ray AIR), an open-source toolkit for building large-scale end-to-end ML applications.

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Machine Learning Data Science Deep Learning Data Ingestion 98

AWS Machine Learning Blog

NOVEMBER 22, 2023

Similar to the rest of the industry, the advancements of accelerated hardware have allowed Amazon teams to pursue model architectures using neural networks and deep learning (DL). About the Authors Abhinandan Patni is a Senior Software Engineer at Amazon Search. You can find him on LinkedIn.

LLM 109

LLM Software Engineer Auto-complete Neural Network 109

Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

Pickl AI

JULY 10, 2024

Understanding AI and Machine Learning Artificial Intelligence (AI) is the simulation of human intelligence in machines designed to think and act like humans. AI encompasses various technologies and applications, from simple algorithms to complex neural networks. Key Features: Comprehensive coverage of Machine Learning models.

Machine Learning 52

Machine Learning Neural Network Deep Learning NLP 52

Becoming Human

MAY 15, 2023

Things to be learned: Ensemble Techniques such as Random Forest and Boosting Algorithms and you can also learn Time Series Analysis. Deep Learning Deep Learning is a subfield of machine learning that focuses on training deep neural networks with multiple layers to improve performance on complex tasks.

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Data Science Neural Network Machine Learning Python 75

AWS Machine Learning Blog

MARCH 30, 2023

The model extracts features from the image using a convolutional neural network. To learn more, visit Amazon SageMaker Data Labeling and schedule a consultation today. About the authors Jonathan Buck is a Software Engineer at Amazon Web Services working at the intersection of machine learning and distributed systems.

Machine Learning 93

Machine Learning Convolutional Neural Networks Neural Network Computer Vision 93

AWS Machine Learning Blog

JUNE 7, 2023

Libraries such as DeepSpeed (an open-source deep learning optimization library for PyTorch) address some of these challenges, and can help accelerate model development and training. Mahadevan obtained his PhD in Mechanical Engineering from the Massachusetts Institute of Technology and has over 25 patents and publications to his credit.

Large Language Models 77

Large Language Models BERT Deep Learning Neural Network 77

Pickl AI

NOVEMBER 28, 2024

For example, in neural networks, data is represented as matrices, and operations like matrix multiplication transform inputs through layers, adjusting weights during training. Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible.

Machine Learning 52

Machine Learning Neural Network ML Engineer Algorithm 52

Marktechpost

JULY 5, 2024

These courses cover foundational topics such as machine learning algorithms, deep learning architectures, natural language processing (NLP), computer vision, reinforcement learning, and AI ethics. It includes real-world projects like building neural networks and image classifiers, culminating in a completion certificate.

Data Science 60

Data Science Machine Learning Data Analysis Software Engineer 60

ODSC - Open Data Science

APRIL 10, 2023

Deep Learning with PyTorch and TensorFlow part 1 and 2 Dr. Jon Krohn | Chief Data Scientist | Nebula.io Introduction to scikit-learn: Machine Learning in Python Thomas J. Intermediate Machine Learning with scikit-learn: Pandas Interoperability, Categorical Data, Parameter Tuning, and Model Evaluation Thomas J.

Software Engineer 52

Software Engineer Categorization Data Science Machine Learning 52

How to Learn Machine Learning

MAY 14, 2023

To help you get started, you can follow this Machine Learning Tutorial that covers various real-world applications and projects: – Machine Learning Project Ideas and Tutorials 4. In addition to deep learning, it’s beneficial to specialize in a specific area or technique within machine learning.

Machine Learning 52

Machine Learning Convolutional Neural Networks Natural Language Processing Deep Learning 52

Pickl AI

OCTOBER 20, 2024

Step-by-Step Guide to Learning AI in 2024 Learning AI can seem daunting at first, but by following a structured approach, you can build a solid foundation and gain the skills needed to thrive in this field. This step-by-step guide will take you through the critical stages of learning AI from scratch. Let’s dive in!

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Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 52

Pickl AI

OCTOBER 2, 2024

Techniques such as Machine Learning and Deep Learning enable better variant interpretation, disease prediction, and personalised medicine. Recurrent Neural Networks (RNNs): Suitable for sequential Data Analysis like DNA sequences where the order of nucleotides matters.

Neural Network 52

Neural Network Deep Learning Machine Learning Artificial Intelligence 52

ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Advanced ML Algorithms & EnsembleMethods Hyperparameter Tuning & Model Optimization AutoML & Real-Time MLSystems Explainable AI & EthicalAI Time Series Forecasting & NLP Techniques Who Should Attend: ML Engineers, Data Scientists, and Technical Practitioners working on production-level ML solutions.

Data Scientist 52

Data Scientist Machine Learning Large Language Models ML Engineer 52

Google Research AI blog

MAY 19, 2023

Posted by Natalia Ponomareva and Alex Kurakin, Staff Software Engineers, Google Research Large machine learning (ML) models are ubiquitous in modern applications: from spam filters to recommender systems and virtual assistants. These models achieve remarkable performance partially due to the abundance of available training data.

ML 102

ML Neural Network Algorithm Machine Learning 102

ODSC - Open Data Science

OCTOBER 4, 2024

His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, and deep reinforcement learning algorithms.

Data Science 52

Data Science Machine Learning Neural Network Software Engineer 52

ODSC - Open Data Science

SEPTEMBER 27, 2023

Get ready for 300+ hours of hands-on training sessions, workshops, and talks on Generative AI, LLMs, MLOps, Machine Learning, Deep Learning, and more. You’ll discuss tensors, neural networks, GPUs and much more. ODSC West is less than a month away!

Neural Network 52

Neural Network Data Scientist Machine Learning Data Science 52

Pickl AI

AUGUST 21, 2023

The Data Science Roadmap: Navigating Your Path to Success Step 1: Learning About Programming or Software Engineering A strong foundation in programming languages like Python , R, or Java is essential. Stay updated with the latest advancements in machine learning, deep learning, and Data Science technologies.

Data Scientist 52

Data Scientist Data Science Software Engineer Machine Learning 52

AWS Machine Learning Blog

MAY 31, 2023

PyTorch supports dynamic computational graphs, enabling network behavior to be changed at runtime. This provides a major flexibility advantage over the majority of ML frameworks, which require neural networks to be defined as static objects before runtime. Be sure to try it out! She is passionate about innovation and inclusion.

ML 101

ML Auto-classification Auto-complete Natural Language Processing 101

Google Research AI blog

DECEMBER 7, 2022

Posted by Krishna Giri Narra, Software Engineer, Google, and Chiyuan Zhang, Research Scientist, Google Research Ad technology providers widely use machine learning (ML) models to predict and present users with the most relevant ads, and to measure the effectiveness of those ads. accuracy) with large computational overheads.

Neural Network 52

Neural Network Algorithm Deep Learning Computer Vision 52

The MLOps Blog

JUNE 27, 2023

With extensive language support and integration with major deep learning frameworks, the Model Hub simplifies the integration of pre-trained models and libraries into existing workflows, making it a valuable resource for researchers, developers, and data scientists. Monitor the performance of machine learning models.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

ODSC - Open Data Science

OCTOBER 18, 2023

The talk will be geared towards a generic computer science audience, though some basic knowledge of machine learning with neural networks will be a useful prerequisite. Machine Learning Has Become Necromancy Mark Saroufim | Author | Breaking Stagnation Machine learning has undergone a profound transformation with open source.

Machine Learning 52

Machine Learning Software Engineer Data Science Data Scientist 52