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Deep Learning News
Medium
5d
119
How Cyber Security Help Business
- AI, specifically machine learning and deep learning, plays a significant role in driving its capabilities.
- Machine learning enables computers to learn patterns and make decisions based on data.
- Deep learning introduces neural networks that can learn complex patterns and representations from data.
- AI has the potential to revolutionize industries and improve everyday lives.
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Marktechpost
5d
345
Microsoft Researchers Propose DiG: Transforming Molecular Modeling with Deep Learning for Equilibrium Distribution Prediction
- Advances in deep learning have revolutionized molecule structure prediction, but real-world applications often require understanding equilibrium distributions rather than just single structures.
- Researchers from Microsoft Research AI4Science, Beijing, China; University of Science and Technology of China, Microsoft Quantum, Redmond, WA, USA; and Microsoft Research AI4Science, Berlin, Germany, have developed Distributional Graphormer (DiG), a deep learning framework aimed at predicting the equilibrium distribution of molecular systems.
- DiG, a deep learning framework, extends beyond predicting single molecular structures to estimating their equilibrium distributions.
- DiG revolutionizes molecular sciences by predicting equilibrium distributions efficiently, enabling diverse molecular sampling crucial for understanding structure-function relationships and designing molecules and materials.
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Semiengineering
5d
254
Image Credit: Semiengineering
Using Deep Learning ADC For Defect Classification For Automatic Defect Inspection
- Traditional manual defect review after automated optical inspection (AOI) is a challenging task for operators and engineers.
- Automatic defect classification (ADC) can reduce the number of defect images reviewed by operators and integrated with AOI engines.
- Onto Innovation’s TrueADC software product supports CNN, DNN, and KNN algorithms for building ADC classifiers.
- Using multiple models with unique algorithms improves classification and meets industry requirements for accuracy and classification rate.
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Medium
5d
262
Image Credit: Medium
History of Deep Learning
- Ludwig Wittgenstein's ideas about language and thought set the stage for computer understanding of language.
- In the late 1980s and 1990s, Geoffrey Hinton introduced backpropagation algorithm and Yann LeCun developed Convolutional Neural Networks (CNNs), leading to renewed interest in AI.
- In the 2000s, Hinton introduced deep belief networks, advancing unsupervised learning.
- The modern deep learning revolution was ignited with the unveiling of AlexNet in 2012 and the introduction of Generative Adversarial Networks (GANs) by Ian Goodfellow in 2014.
- Subsequent advancements include the application of recurrent neural networks (RNNs) to natural language processing and the introduction of Capsule Networks by Geoffrey Hinton in 2017.
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Medium
5d
184
Image Credit: Medium
AI vs ML vs DL (Machine Learning, Artificial Intelligence, Deep Learning)
- AI encompasses the broad field of creating machines that can perform tasks requiring human-like intelligence.
- ML is a subset of AI that focuses on enabling machines to learn from data and improve their performance over time without being explicitly programmed.
- DL is a subset of ML that uses artificial neural networks with multiple layers (hence “deep”) to learn representations of data.
- Understanding the distinctions between AI, ML, and DL is essential for navigating the rapidly evolving landscape of technology and innovation.
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Medium
5d
234
Image Credit: Medium
Simplest explanation of ‘Why AI’ and ‘What’s Machine Learning?’
- AI is meant to automate tasks, but it cannot handle estimating or predicting in tasks that are not well-defined.
- Machine learning is a technique that enables AI to handle such tasks.
- Traditional programming is used for well-defined tasks, while machine learning is used for ill-defined tasks.
- Machine learning uses a collection of weights to examine data, extract what matters, and make accurate predictions or estimates.
- This approach can handle some business scenarios, like the loan application process mentioned in the article.
- However, it falls short when it comes to investment counseling and customer service within the banking industry.
- For those scenarios, an advanced form of learning called Deep Learning is needed to build the right AI model.
- Traditional programming can automate tasks using clear, concise instructions.
- AI is necessary when tasks require judgment, prediction, estimation, or complicated relationships between factors.
- Machine learning models and weights can replicate the judgment of a human with even greater accuracy.
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Medium
5d
394
Title: The Art of Digital Detox: Reclaiming Balance in a Hyperconnected World
- 1. Reduced Stress and Anxiety: Excessive screen time and constant connectivity contribute to increased stress and anxiety. Taking a break from devices helps relax the mind and reduce tension.
- 2. Improved Sleep Quality: Disconnecting from screens before bedtime allows the brain to wind down, leading to better sleep quality and overall well-being.
- 3. Increased Productivity and Creativity: Unplugging from digital devices enhances focus, productivity, and stimulates fresh perspectives and ideas.
- 4. Stronger Relationships: Prioritizing face-to-face interactions over virtual ones strengthens relationships and deepens connections.
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23 Likes
Medium
6d
53
Image Credit: Medium
GPT4 Omni — So much more than just a voice assistant
- GPT4 Omni is a revolutionary model that goes beyond being just a voice assistant.
- It is a single model capable of processing and generating text, audio, and image modalities.
- The possibilities of GPT4 Omni are immense and surpass anything currently available.
- While access to all modalities in the API is not yet released, the model is already generating 3D images.
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3 Likes
Medium
6d
160
Image Credit: Medium
Breaking News: OpenAI's Groundbreaking GPT-4o and More!
- OpenAI has released GPT-4o, the latest version of the ChatGPT series.
- GPT-4o can chat about various topics, understand images, and communicate in over 50 languages.
- OpenAI has also introduced new tools like the Assistants API and improved vision and voice capabilities.
- These updates come at lower prices, and OpenAI continues to work on enhancing AI technology.
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Medium
6d
408
Image Credit: Medium
A Glossary of AI Terms and Buzzwords
- Here’s a free glossary of AI terminologies you can bookmark for future reference.
- Understanding AI terms is important in today’s digital age.
- AI is being integrated into more aspects of our daily lives.
- This glossary lists over 60 AI terminologies alphabetically.
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Pymnts
6d
408
Image Credit: Pymnts
AI Explained: Deep Learning Lets AI Tackle Complex Tasks
- Deep learning is a cutting-edge approach to AI that mimics the human brain.
- It uses artificial neural networks to automatically discover patterns in large datasets.
- Deep learning has advanced natural language processing and creativity in art and music.
- However, it has limitations such as the need for large amounts of data and potential biases.
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Medium
6d
111
Image Credit: Medium
Training a single perceptron from scratch
- Activation function is the function which takes an input (here it takes a) and returns values within a range.
- Weights are the strengths which tells us which input is more important for predicting the output.
- Backpropagation is the algorithm used to update the weights and biases of a neural network going back through the neural network.
- The role of bias is to shift the result to a bit up or down to get a non-zero value.
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Medium
6d
210
Image Credit: Medium
Introduction to Large Language Models (LLMs): The Powerhouse of Generative AI
- Large Language Models (LLMs) are deep learning models trained on massive corpus of language data to perform various natural language processing (NLP) tasks.
- Generative AI relies heavily on LLMs and employs transfer learning and fine tuning to train the models more efficiently.
- LLMs are the backbone of NLP products such as text generation, chatbots, summarization, code generation, etc.
- The parameters in an LLM are the trainable weights in a model, their number determines the amount of computational resources required for a training session.
- Encoder based models, decoder based models, and encoder-decoder based models are examples of LLMs.
- ChatGPT, a product by OpenAI, is trained on generative pre-training, supervised fine tuning, and reinforcement learning through human feedback (RLHF).
- Factual inaccuracies, biases, and hallucination are some limitations of LLMs that need to be addressed.
- LLMs play a significant role in generative AI, empowering it to perform diverse tasks from language to image and audio data.
- Theoretical understanding of LLMs sets the foundation for exploring their practical applications in future.
- The article concludes by thanking the training resources used by the author and inviting feedback and questions from readers.
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Medium
6d
364
Image Credit: Medium
What is PyTorch?
- PyTorch is an open-source deep learning platform developed by Facebook.
- It was built on top of a deep learning library called KarpathyNet and was officially announced in 2016.
- PyTorch uses tensors as its fundamental data structure and employs a dynamic computation graph.
- It is known for its flexibility, ease of use, and has a strong community and ecosystem.
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21 Likes
Medium
6d
115
Image Credit: Medium
Synthetic Data: A Strategic Solution to Data Imbalance
- Data imbalance is a major issue in machine learning models, causing inefficiency in forecast and generalization.
- Synthetic data generation is an effective technique to address data imbalance, balancing class distribution and improving learning.
- YData is a comprehensive toolkit that enables synthetic data generation for tackling data imbalance.
- Using YData, diverse datasets of high-quality training samples can be generated, leading to fair and accurate models.
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