Description: Deep learning is a cutting-edge form of machine learning inspired by the architecture of the human brain, but it doesn't have to be intimidating. With TensorFlow, coupled with the Keras API and Python, it's easy to train, test, and tune deep learning models without knowing advanced math. To start this Skill Path, sign up for Codecademy Pro. Description: Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

Probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and expressing uncertainty about unknowns, but it lacks flexibility. Deep learning (DL) is an alternative framework for learning from data that has achieved great empirical success in recent years. DL offers great flexibility, but it lacks the interpretability and calibration of PGM. This thesis develops deep probabilistic graphical modeling (DPGM.) DPGM consists in leveraging DL to make PGM more flexible. DPGM brings about new methods for learning from data that exhibit the advantages of both PGM and DL. We use DL within PGM to build flexible models endowed with an interpretable latent structure. One model class we develop extends exponential family PCA using neural networks to improve predictive performance while enforcing the interpretability of the latent factors. Another model class we introduce enables accounting for long-term dependencies when modeling sequential data, which is a challenge when using purely DL or PGM approaches. Finally, DPGM successfully solves several outstanding problems of probabilistic topic models, a widely used family of models in PGM. DPGM also brings about new algorithms for learning with complex data. We develop reweighted expectation maximization, an algorithm that unifies several existing maximum likelihood-based algorithms for learning models parameterized by neural networks. This unifying view is made possible using expectation maximization, a canonical inference algorithm in PGM. We also develop entropy-regularized adversarial learning, a learning paradigm that deviates from the traditional maximum likelihood approach used in PGM. From the DL perspective, entropy-regularized adversarial learning provides a solution to the long-standing mode collapse problem of generative adversarial networks, a widely used DL approach.

Data poisoning is a type of adversarial attack on training data where an attacker manipulates a fraction of data to degrade the performance of machine learning model. Therefore, applications that rely on external data-sources for training data are at a significantly higher risk. There are several known defensive mechanisms that can help in mitigating the threat from such attacks. For example, data sanitization is a popular defensive mechanism wherein the learner rejects those data points that are sufficiently far from the set of training instances. Prior work on data poisoning defense primarily focused on offline setting, wherein all the data is assumed to be available for analysis. Defensive measures for online learning, where data points arrive sequentially, have not garnered similar interest. In this work, we propose a defense mechanism to minimize the degradation caused by the poisoned training data on a learner's model in an online setup. Our proposed method utilizes an influence function which is a classic technique in robust statistics. Further, we supplement it with the existing data sanitization methods for filtering out some of the poisoned data points. We study the effectiveness of our defense mechanism on multiple datasets and across multiple attack strategies against an online learner.

Please note this course has now been updated to Unity 2020.1. This is the long-awaited sequel to the Complete Unity Developer - one of the most popular e-learning courses on the internet! You will benefit from the fact we have already taught over 700,000 students programming and game development, many shipping commercial games as a result. Unity is an incredible 3D package used for making video games, architectural and medical imaging and more. The challenge is that it's big and complicated to use, especially for complete beginners to coding and game development.

The $200 Logitech Circle View Doorbell is aimed at a very specific audience: Homeowners with wired doorbells who've embraced Apple's rapidly growing HomeKit smart home ecosystem. This is not a cross-platform product: Android users need not apply; nor is there any support for Amazon Alexa or Google Assistant. The upside of this approach is that it allowed Logitech to take full advantage of what HomeKit has to offer, including HomeKit Secure Video, which uses the Apple hardware in your home to process captured video locally, versus uploading it to a server in the cloud that you have no real control over (you can upload encrypted video to your iCloud account, but you will be the only person with access--Apple won't be able to decrypt the files). I fashioned my own mounting block to compensate for the clapboard siding on my home. The downside, of course, is that you'll need to have that hardware in the first place: A HomePod, HomePod mini, Apple TV, or an iPad (provided it never leaves your house).

Would you like to learn how to tell your robotics/AI story to the public? Robohub and AIhub are testing a new workshop to train you as the next generation of communicators. You will learn to quickly create your story and shape it to any format, from short tweets to blog posts and beyond. In addition, you will learn how to communicate about robotics/AI in a realistic way (avoiding the hype), and will receive tips from top communicators, science journalists and early career researchers. If you feel like being one of our beta testers, join this free workshop to experience how much impact science communication can have on your professional journey!

The field of Weakly Supervised Learning (WSL) has recently seen a surge of popularity, with numerous papers addressing different types of "supervision deficiencies". In WSL use cases, a variety of situations exists where the collected "information" is imperfect. The paradigm of WSL attempts to list and cover these problems with associated solutions. In this paper, we review the research progress on WSL with the aim to make it as a brief introduction to this field. We present the three axis of WSL cube and an overview of most of all the elements of their facets. We propose three measurable quantities that acts as coordinates in the previously defined cube namely: Quality, Adaptability and Quantity of information. Thus we suggest that Biquality Learning framework can be defined as a plan of the WSL cube and propose to re-discover previously unrelated patches in WSL literature as a unified Biquality Learning literature.

We consider the problem of the Zinkevich (2003)-style dynamic regret minimization in online learning with exp-concave losses. We show that whenever improper learning is allowed, a Strongly Adaptive online learner achieves the dynamic regret of $\tilde O(d^{3.5}n^{1/3}C_n^{2/3} \vee d\log n)$ where $C_n$ is the total variation (a.k.a. path length) of the an arbitrary sequence of comparators that may not be known to the learner ahead of time. Achieving this rate was highly nontrivial even for squared losses in 1D where the best known upper bound was $O(\sqrt{nC_n} \vee \log n)$ (Yuan and Lamperski, 2019). Our new proof techniques make elegant use of the intricate structures of the primal and dual variables imposed by the KKT conditions and could be of independent interest. Finally, we apply our results to the classical statistical problem of locally adaptive non-parametric regression (Mammen, 1991; Donoho and Johnstone, 1998) and obtain a stronger and more flexible algorithm that do not require any statistical assumptions or any hyperparameter tuning.

Conservative mechanism is a desirable property in decision-making problems which balance the tradeoff between the exploration and exploitation. We propose the novel \emph{conservative contextual combinatorial cascading bandit ($C^4$-bandit)}, a cascading online learning game which incorporates the conservative mechanism. At each time step, the learning agent is given some contexts and has to recommend a list of items but not worse than the base strategy and then observes the reward by some stopping rules. We design the $C^4$-UCB algorithm to solve the problem and prove its n-step upper regret bound for two situations: known baseline reward and unknown baseline reward. The regret in both situations can be decomposed into two terms: (a) the upper bound for the general contextual combinatorial cascading bandit; and (b) a constant term for the regret from the conservative mechanism. We also improve the bound of the conservative contextual combinatorial bandit as a by-product. Experiments on synthetic data demonstrate its advantages and validate our theoretical analysis.

Companies are increasingly using algorithms to manage and control individuals not by force, but rather by nudging them into desirable behavior -- in other words, learning from their personalized data and altering their choices in some subtle way. Since the Cambridge Analytica Scandal in 2017, for example, it is widely known that the flood of targeted advertising and highly personalized content on Facebook may not only nudge users into buying more products, but also to coax and manipulate them into voting for particular political parties. University of Chicago economist Richard Thaler and Harvard Law School professor Cass Sunstein popularized the term "nudge" in 2008, but due to recent advances in AI and machine learning, algorithmic nudging is much more powerful than its non-algorithmic counterpart. With so much data about workers' behavioral patterns at their fingertips, companies can now develop personalized strategies for changing individuals' decisions and behaviors at large scale. These algorithms can be adjusted in real-time, making the approach even more effective.