کتاب اصول یادگیری عمیق با پایتون (انگلیسی) 255 صفحه، سال 2016
#کتاب #یادگیری_عمیق #پایتون
#Book #Deep_Learning #DeepLearning #DL #Python
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@machinelearning_tuts
@selfdrivecar
@autonomousvehicle

#Deep_Learning_for_Medical #Image #Processing:Overview, Challenges and Future
#paper
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@machinelearning_tuts

#Deep_Learning_in_Bioinformatics
#paper
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@machinelearning_tuts

🔻 Massively Scaling Reinforcement Learning with SEED RL

Reinforcement learning (RL) has seen impressive advances over the last few years as demonstrated by the recent success in solving games such as Go and Dota 2. Models, or agents, learn by exploring an environment, such as a game, while optimizing for specified goals. However, current RL techniques require increasingly large amounts of training to successfully learn even simple games, which makes iterating research and product ideas computationally expensive and time consuming.

📌 Via: @cedeeplearning

link: https://ai.googleblog.com/

#reinforcement
#RL
#deep_learning
#architecture
#training

🔹AlphaFold: Improved #protein structure #prediction using potentials from #deep_learning

https://deepmind.com/research/publications/AlphaFold-Improved-protein-structure-prediction-using-potentials-from-deep-learning
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Via: Cutting-edge Deep Learning
Credit: deepmind.com

#deepmind
#machinelearning
#neuralnetworks

🔹Understanding Generative Adversarial Networks (GANs)

Yann LeCun described it as “the most interesting idea in the last 10 years in #Machine_Learning”. Of course, such a compliment coming from such a prominent researcher in the #deep_learning area is always a great advertisement for the subject we are talking about! And, indeed, #Generative Adversarial #Networks (#GANs for short) have had a huge success since they were introduced in 2014 by Ian J. #Goodfellow and co-authors in the article Generative Adversarial Nets.

📌Via: @cedeeplearning

link: https://towardsdatascience.com/understanding-generative-adversarial-networks-gans-cd6e4651a29

🔹Structured learning and GANs in TF, another viral face-swapper, optimizer benchmarks, and more...

This week in #deep_learning we bring you a GAN library for TensorFlow 2.0, another viral #face-swapping app, an #AI Mahjong player from Microsoft, and surprising results showing random architecture search beating neural architecture search. You may also enjoy an interview with Yann LeCun on the AI Podcast, a primer on #MLIR from Google, a few-shot face-#swapping #GAN, benchmarks for recent optimizers, a structured learning #framework for #TensorFlow, and more!

📌Via: @cedeeplearning

link: https://www.deeplearningweekly.com/issues/deep-learning-weekly-issue-124.html

🔻When not to use deep learning

Despite #DL many successes, there are at least 4 situations where it is more of a hindrance, including low-budget problems, or when explaining #models and #features to general public is required.
So when not to use #deep_learning?

1. #Low-budget or #low-commitment problems

2. Interpreting and communicating model parameters/feature importance to a general audience

3. Establishing causal mechanisms

4. Learning from “#unstructured” features

📌Via: @cedeeplearning

link: https://www.kdnuggets.com/2017/07/when-not-use-deep-learning.html/2

🔻Popular Deep Learning #Courses of 2019🔻

With #deep_learning and #AI on the forefront of the latest applications and demands for new business directions, additional #education is paramount for current machine learning engineers and #data_scientists. These courses are famous among peers, and will help you demonstrate tangible proof of your new skills.

📌Via: @cedeeplearning

https://www.kdnuggets.com/2019/12/deep-learning-courses.html

🔻Predictions for Deep Learning in 2017

The first hugely successful consumer application of deep learning will come to market, a dominant #open-source deep-learning tool and library will take the developer community by storm, and more Deep Learning predictions.

Deep learning is all the rage as we move into 2017. Grounded in #multilayer #neural_networks, this technology is the foundation of artificial intelligence, #cognitive computing, and #real-time streaming #analytics in many of the most disruptive new #applications.
For data scientists, #deep_learning will be a top professional focus going forward. Here are my #predictions for the chief #trends in deep learning in the coming year: (🔻click on the link to the rest)

link: https://www.kdnuggets.com/2016/12/ibm-predictions-deep-learning-2017.html

📌Via: @cedeeplearning

🔻Top 10 Deep Learning Projects on #Github

The top 10 #deep_learning projects on Github include a number of #libraries, #frameworks, and education resources. Have a look at the tools others are using, and the resources they are learning from.
1. Caffe
2. Data Science IPython Notebooks
3. ConvNetJS
4. Keras
5. MXNet
6. Qix
7. Deeplearning4j
8. Machine Learning Tutorials
9. DeepLearnToolbox
10. LISA Lab Deep Learning Tutorials

link: https://www.kdnuggets.com/2016/01/top-10-deep-learning-github.html

📌Via: @cedeeplearning