Hey guys, in this episode I explain what the Reinforcement Learning. In the episode I give some application of where Reinforcement Learning is being used and then I explain the basic concepts of an agent, an environment, a reward, the states and transitions. Then, I talk in a really high level about the ideia behind the main algorithms like Q-Learning, Deep-Learning, Actor Critic, PPO, DDPG... In the end I talk about the advanced Reinforcement Learning techniques that we have, like curriculum learning, hierarchical learning, imitation learning, meta learning and multi agent RL.

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Github code: https://github.com/filipelauar/projects/tree/main/Reinforcement%20Learning

Hey guys, in this episode of the podcast I explain what are neural networks and how they work. In the episode I talk about the relationship between our brain and the neural network architecture and also how they are trained, using the famous algorithm of back-propagation. After understanding how neural nets work, I discuss if these "artificial intelligence" algorithm are really intelligent.

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Tensorflow playground: https://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle®Dataset=reg-plane&learningRate=0.03®ularizationRate=0&noise=0&networkShape=4,2&seed=0.24800&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false

Hey guys, in this episode a have as guest the ex Oxford researcher Shrinivasan Sankar. In the episode Shrini talk about his research on Deep Learning and Active Learning, his transition from academia to an agroTech startup and also about his youtube channel, AI Bites. We also talk about how to enter/start in this Data Science and Deep Learning field.

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Shrini's Linkedin: https://www.linkedin.com/in/shrinivasan-sankar-2885016/

AI Bites YouTube channel: https://www.youtube.com/c/AIBites

Facebook's paper (Data2Vec): https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language

Hey guys, in this episode I have as guest Asya Grechka, a PhD student and my professor at Sorbonne. In the episode we mainly talk about Asya's research in generative models and GANs, but about some other interesting subjects, like general artificial intelligence. Besides being a PhD in Deep Learning, Asya also does professional climbing and was 2x finalist of the American Ninja Warrior, in the episode she told us about the experience.

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Github code about image editing made by Asya:

Hey guys, in this episode I talk about the pre training process of the state of the art NLP and computer vision transformer architectures. Since 2017 we train NLP (BERT, GPT, ELECTRA) networks with a masked language model using a self-supervised procedure, and now (since 2022) we are also able to train vision (MAE) networks using the same masked language model procedure. This way of self-supervised pre training enable us to train accurate models that really understands semantic and context without labeled data. I also talk about a tabular transformed architecture (TabTransformer - 2020) using the same approach achieve state of the art results compared to ensemble methods.

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BERT paper: https://arxiv.org/pdf/1810.04805.pdf

GPT3 paper: https://arxiv.org/pdf/2005.14165.pdf

ELECTRA paper: https://arxiv.org/pdf/2003.10555.pdf

MAE paper: https://arxiv.org/pdf/2111.06377.pdf

TabTransformers paper: https://arxiv.org/pdf/2012.06678.pdf

Hey guys, in this episode I talk about the how to attack a neural network and also how to defend your network against these attacks. In the episode I give the example of attacking autonomous vehicles, how people manage to create images that make this vehicle think that a person is a tree or a car, for instance, explaining also the methods that do it. I also talk about how to defend your network from these attacks using different techniques of adversarial defenses.

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Code: https://github.com/filipelauar/projects/blob/main/adversarial_attacks_pytorch.ipynb

Hey guys, in this episode I explain how an autonomous vehicle can learn to drive in a specific city and be able to generalize the driving knowledge to any other city. To explain it, I show the concept of of semi-supervised learning and domain adaptation. I explain the the ideia with the first proposed architecture and what we have today as state of the art for this problem. 

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Code: https://github.com/filipelauar/projects/blob/main/domain_adaptation_semi_supervised_learning_MNIST_pytorch.ipynb