Bigdata, data, dataanalyticsreport, ai & much more…
BigData News Sunday, July 15
- Transfer Learning –Deep Learning for Everyone
- Top 10 Big Data Tools in 2018
Bigdata, data, DataAnalyticsReport
- Global Bigdata Conference
AI, MachineLearning, Deeplearning, DataScience, ArtificialIntelligence, NLP, BigData, IoT, Cassandra, NoSQL
- Sponsorship Opportunities For @CloudEXPO New York Open
BigData, AI, DevOps, IoT
- A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
- What’s the Difference Between AI, Machine Learning, and Deep Learning?
- Comprehensive Repository of Data Science and ML Resources – Data Science Central
DataScience, MachineLearning, NeuralNetworks, abdsc, BigData, AI, DeepLearning, DataScientists
BigData, Strategy, StartSmall, analytics
- Transfer Learning (TL), a method of reusing previously trained deep neural nets promises to make these applications available to everyone, even those with very little labeled data.
- The central concept is to use a more complex but successful pre-trained DNN model to transfer its learning to your more simplified (or equally but not more complex) problem.
- If you have a fairly large amount of labeled data (estimates range down to 1,000 images per class but are probably larger) then you can utilize the more accurate TL method by creating a wholly new model using the weights and hyperparameters of the pre-trained model.
- In simplified TL the pre-trained transfer model is simply chopped off at the last one or two layers.
- One Other Interesting Application of Transfer Learning – – This may not have much commercial application but its interesting to know that you can use TL to combine two separate pre-trained models to achieve unexpected artistic results.
@DataScienceCtrl: Transfer Learning �Deep Learning for Everyone https://t.co/B40OaMChQW
- Big data has been a game changer for organizations across industries and revenue size.
- Big data helps companies to process data of great complexity and size at a speed and accuracy that helps in making better decision.
- If a company has to sift and sort through some millions of records to pick out that one faulty record that the auditor is asking for, then big data technology can help it index and search through those legacy records in record time.
- There are many more scenarios where big data can propel a companys success and help it make its processes smoother and more efficient.
- The following big data tools are in great use today and each of them offer a specific niche advantage to the firm using it.
@DataanalyticsR: #Bigdata helps companies to process #data of great complexity and size at a speed and accuracy that helps in making better decision. #DataAnalyticsReport https://t.co/lqgTXc2NW4
- A data scientist tries to fit multiple models.
- In short, automated machine learning saves a lot of data scientist time.
- Data scientist spends lesser time in spending on model building and more time on evaluation.
- It helps them to build decent machine learning models without deep-diving into the mathematics of data science.
- There are niche companies like Data Robot who specialise in this area and are becoming mainstream.
@bigdataconf: Top Trends in #AI in 2018 https://t.co/LYMCIbNlVE #MachineLearning #Deeplearning #DataScience #ArtificialIntelligence #NLP #BigData #IoT #Cassandra #NoSQL
@machinelearnflx: Object Detection & Recognition Using Deep Learning in OpenCV https://t.co/fJC2rAOVE2 #machinelearning #ad
@TheIoT: Sponsorship Opportunities For @CloudEXPO New York Open | #BigData #AI #DevOps… https://t.co/lWKBPxTzKN #IoT
- The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data.
- However, it has thus far seen limited application to sequential data, and, as we demonstrate, existing recurrent VAE models have difficulty modeling sequences with long-term structure.
- To address this issue, we propose the use of a hierarchical decoder, which first outputs embeddings for subsequences of the input and then uses these embeddings to generate each subsequence independently.
- This structure encourages the model to utilize its latent code, thereby avoiding the posterior collapse problem which remains an issue for recurrent VAEs.
- We apply this architecture to modeling sequences of musical notes and find that it exhibits dramatically better sampling, interpolation, and reconstruction performance than a flat baseline model.
@GoogleAI: At 12PM, @fjord41 will be at the #ICML2018 Google booth to show an exploration of machine learning’s role in the process of creating music (https://t.co/fuIvqbt935), and @NalKalchbrenner will demo a Sparse WaveRNN speaking on a Pixel 2 mobile phone (https://t.co/XztEyzbXBW). https://t.co/gsEQEh5z4Z
- Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
- Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
- Lets look at a couple of problems to see how deep learning is different from simpler neural networks or other forms of machine learning.
- There are many techniques for AI, but one subset of that bigger list is machine learning let the algorithms learn from the data.
- Finally, deep learning is a subset of machine learning, using many-layered neural networks to solve the hardest (for computers) problems.
@java: What’s the Difference Between AI, #MachineLearning, and #DeepLearning https://t.co/zqM9tACvxN https://t.co/BpnKGg8ZOo
@CSRjames: Is privacy possible with machine learning? @PwC_Canada’s Director of Cybersecurity & Privacy @JordanProkopy shares her thoughts: https://t.co/ajWlVmG91g
@KirkDBorne: Comprehensive Repository of #DataScience and #MachineLearning Resources, including “22 Great Articles About #NeuralNetworks” 👉 https://t.co/qz1nRPwWRL #abdsc #BigData #AI #DeepLearning #DataScientists https://t.co/KrTL5m4UMP
@SavinzDeals: #BigData Winning #Strategy For Enterprises: #StartSmall https://t.co/81ukyygpBI #analytics