Notes on #machinelearning

🌎 MORE LEARN

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Korbit is now launching the world’s first deep learning course taught by an interactive deep learning tutor. The online course is a four-week-long introduction to #machinelearning and deep learning, featuring lectures from Mila professors Yoshua Bengio, Laurent Charlin, Audrey Durand and Aaron Courville, and includes over 100 interactive exercises (question-answering exercises, drag-and-drop exercises and mathematical problems).

The #deeplearning tutor Korbi guides students through the course with a problem-solving approach and offers them different exercises, hints and visual diagrams based on their individual level of understanding and unique learning profile. The course is free and available for everyone at: www.korbit.ai/machinelearning.

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🔸Inside TensorFlow: Summaries and TensorBoard

🌐 https://www.youtube.com/watch?v=OI4cskHUslQ

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Another great paper from Samsung AI lab! Egor Zakharovdl et al (Few-Shot Adversarial Learning of Realistic Neural Talking Head Models). animate heads using only few shots of target person (or even 1 shot). Keypoints, adaptive instance norms and #GANs, no 3D face modelling at all.

📝 https://arxiv.org/abs/1905.08233

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A few weeks ago, a friend of mine asked me "Which papers can I read to catch up with the latest trends in modern #NLP?". 🏃‍♂️👨‍🎓 I compiled a list of papers and resources for him 📚 and thought it would be great to share it!

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I do currently use Python but, since it's quite popular among data scientists, I've looked into it and have read several #book s on #Python:

- A Primer on Scientific Programming with Python (Langtangen)
- Python for Data Analysis (McKinney)
- Python Data Science Essentials (Boschetti and Massaron)
- Machine Learning in Python (Bowles)
- Hands-On Predictive Analytics with Python (Fuentes)
- Data Science for Marketing Analytics (Blanchard et al.)
- Bayesian Analysis with Python (Martin)
- Web Scraping with Python (Lawson)

I have found all of them helpful in different ways, including offering different perspectives on data science. Several are well-known, but I cannot critique them as an experienced Python user, so this is just FYI.

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When you read in another white paper or about us "our AI-based solution".
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An advantage probabilistic models such as logistic regression have over hard classifiers is that we are not bound to positive/negative dichotomies.

A customer with purchase probability of .51 may be very different from one with a probability of .99.

Moreover, our model may perform very well in some prediction ranges and fall down badly in others. Having this information can help us diagnose the causes, improve our model and learn about our customers.

What about non-linearities and moderated effects (interactions)? With a little extra work, these can be identified and incorporated into the model.

What about customer heterogeneity (for example)? Any model can be boosted and bagged and mixture modeling is also (for me) an attractive option.

Methods such as logistic regression also have advantages over some other methods when there are more than two classes (groups) and when the sizes of the classes are very different (class imbalance).

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SinGAN: Learning a Generative Model from a Single Natural Image

Shaham et al.: https://lnkd.in/eUWJ6Ta

#ArtificialIntelligence #DeepLearning
#GenerativeAdversarialNetworks
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The Best and Most Current of Modern Natural Language Processing

Blog by Victor Sanh: https://lnkd.in/emch8gG

#NaturalLanguageProcessing #MachineLearning #NLP #DeepLearning #Research

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omplete Deep Learning Drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

GitHub by Marimuthu K.: https://lnkd.in/eTUp4Hi

B. For Business Applications you can see at
Data Science Process https://lnkd.in/fMHtxYP
Data Visualization in Business https://lnkd.in/fYUCzgC
Understand How to answer Why https://lnkd.in/f396Dqg
Know Machine Learning Key Terminology https://lnkd.in/fCihY9W
Understand Machine Learning Implementation https://lnkd.in/f5aUbBM
Machine Learning Applications on Marketing https://lnkd.in/fUDGAQW
Machine Learning Applications on Retail https://lnkd.in/fihPTJf

#machinelearning #analytics #datascience #artificialintelligence

#artificialintelligence #deeplearning #machinelearning #reinforcementlearning

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"Neural network models are inspired by the biological brain—the analogy is that just as the neurons in brains calculate something and are connected, neurons in artificial neural networks also calculate and are connected, forming networks of interconnected neurons, also called units. In reality, the analogy ends there; biological brains are complicated structures, and there is still a lot that we don't know about how they work. So, if someone asks you whether your neural network model works like a brain, the answer is an emphatic, No." Alvaro Fuentes in "Hands-On Predictive Analytics with Python: Master the complete predictive analytics process, from problem definition to model deployment." Packt Publishing.

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Extracting Features from Text - A Step-by-Step NLP Guide to Learn ELMo Python - Prateek Joshi.


#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #python #nlp #tutorial

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End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Researchers from #GoogleAi and #Stanford published work today in #Nature that shows great potential to use machine learning to help catch more lung cancer cases earlier and increase survival likelihood.

Link: https://lnkd.in/fUMtA-3

#LungCancer #Cancer #biolearning #healthcare #DL

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#ArtificialNeuralNetworks (ANN) were supposed to replicate the architecture of the human brain, yet till about a decade ago, the only common feature between #ANN and our brain was the nomenclature of their entities (for instance – neuron). ANN architectures have become extremely useful across industries.

https://bit.ly/2Et1wpl

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Download pdf, or get your copy, of AutoML: Methods, Systems, Challenges.

https://lnkd.in/gnWgVt8
#datascience
#machinelearning


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Which doodles are human-drawn and which are AI-generated? Berkeley researchers Forrest Huang et al created a #neuralnetwork that can generate sketches based on text descriptions:
http://bit.ly/2LZSHJN


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The basics of NLP and real time sentiment analysis with open source tools

#NLP #sentimentanalysis #opensource

The basics of NLP and real time sentiment analysis with open source tools
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Quant types often dismiss qualitative research as subjective and, therefore, unscientific.

Though a quant type myself, I must dissent. Not on the grounds that qualitative research is purely objective, but on the grounds that most quantitative research in any field requires a substantial amount of subjective judgment on the part of the research team, statisticians included.

Were this not the case, there would be little need to reproduce and replicate quantitative research findings, apart from concerns about fraud.

There would be far fewer journal articles, books and conferences, and little need for #meta_analysis. All #AI would give us the same answers, too.

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