Deep Learning
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Semi-supervised learning explained
Using a machine learning model’s own predictions on unlabeled data to add to the labeled data set sometimes improves accuracy, but not always
IBM Trusted AI toolkits for Python combat AI bias
IBM has released Python toolkits for identifying and mitigating against bias in training data and machine learning models
Deep learning frameworks: PyTorch vs. TensorFlow
If you actually need a deep learning model, PyTorch and TensorFlow are both good choices
Automated machine learning or AutoML explained
AutoML frameworks and services eliminate the need for skilled data scientists to build machine learning and deep learning models
5 machine learning tools to ease software development
AI-driven development tools that provide code auto-completion, code vulnerability detection, and even cutting-edge code generation
The best machine learning and deep learning libraries
TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models
TensorFlow 2 review: Easier machine learning
Now more platform than toolkit, TensorFlow has made strides in everything from ease of use to distributed training and deployment
Supervised learning explained
Supervised learning turns labeled training data into a tuned predictive model
Reinforcement learning explained
Reinforcement learning uses rewards and penalties to teach computers how to play games and robots how to perform tasks independently
Natural language processing explained
Deep learning has improved machine translation and other NLP tasks by leaps and bounds
Deep learning explained
Deep neural networks can solve the most challenging problems, but require abundant computing power and massive amounts of data
Machine learning explained
Able to learn from data, machine learning algorithms can solve problems that are too complex to solve with conventional programming
Machine learning algorithms explained
Machine learning uses algorithms to turn a data set into a model. Which algorithm works best depends on the problem
How to model time-series anomaly detection for IoT
Machines fail. By creating a time-series prediction model from historical sensor data, you can know when that failure is coming
Review: Google Cloud AutoML is truly automated machine learning
Google’s AutoML lets you create customized deep learning models without any knowledge of data science or programming
AI, machine learning, and deep learning: Everything you need to know
All about the business benefits, technology frameworks and models, and application of artificial intelligence for better business outcomes
How to use deep learning to write Shakespeare
LSTM recurrent neural networks can be trained to generate free text. Let’s see how well AI can imitate the Bard
Review: Azure Machine Learning challenges Amazon SageMaker
Microsoft Azure’s plush, Python-based environment covers the full machine learning and deep learning development cycle
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