Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses. In this course, you will prepare to take the DP-100 Azure Data Scientist Associate certification exam. You will refresh your knowledge of how to plan and create a suitable working environment for data science workloads on Azure, run data experiments, and train predictive models. In addition, you will recap on how to manage, optimize, and deploy machine learning models into production. You will test your knowledge in a practice exam mapped to all the main topics covered in the DP-100 exam, ensuring you're well prepared for certification success.

Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. A Road map connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them. Machine learning can help with the diagnosis of diseases. Many physicians use chat bot with speech recognition capabilities to discern patterns in symptoms.

Welcome to this course on Probabilistic Deep Learning with TensorFlow! This course builds on the foundational concepts and skills for TensorFlow taught in the first two courses in this specialisation, and focuses on the probabilistic approach to deep learning. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real world datasets. This is a crucial aspect when using deep learning models in applications such as autonomous vehicles or medical diagnoses; we need the model to know what it doesn't know. You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning.

Artificial intelligence in action is still in its infancy. When AI seems real, human, and like someone you'd trust, it's a perplexing reaction. You can tell that Alexa or Siri is a bot, for instance, but what if you couldn't actually tell an AI-generated podcast from one that was entirely human-created? This podcast--created by podcast producer Rev. Yugen Bond alongside some snarky robots--was written with AI technology. It's a fascinating glimpse into what AI potentially holds for content creation. ABOUT THE PODCAST: This minimalist wants more. Enough-ism is about having enough, already. The world is experiencing an awakening. This podcast about mindfulness, meditation, and minimalism is your modern toolkit to keep your spirit right and your soul bright. One candle can light a fire. ABOUT THE HOST: Reverend Yugen Bond is an author and reiki master with a master’s in metaphysical sciences. She once despised meditation, had both too much and nothing to wear, and didn't know how to slow down her thoughts. What a journey it's been. Time to share it with the world, especially with you. CONTACT INFO:  Can’t get enough of Enough-ism? Follow @IAmEnoughism and visit IAmEnoughism.com | Support the show: Buy the "Enough-ism: This Minimalist Wants More" e-book now on Amazon Kindle! For business inquiries, guest requests, and speaking engagements, email enoughismpodcast@gmail.com. 

Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations.

A classical ASR approach treats the process of generating speech as a noisy channel. In this framing, text is drawn from some distribution and statistically transformed into We present a noisy channel generative model speech audio; the speech recognition task is then to invert of two sequences, for example text and speech, this generative model to infer the text most likely to have which enables uncovering the association between given rise to a given speech waveform. This generative the two modalities when limited paired data is model of speech was historically successful (Baker, 1975; available. To address the intractability of the exact Jelinek, 1976; Rabiner, 1989), but has been superseded in model under a realistic data setup, we propose modern discriminative systems by directly modeling the a variational inference approximation. To train conditional distribution of text, given speech (Graves et al., this variational model with categorical data, we 2006; Amodei et al., 2016). The direct approach has the advantage propose a KL encoder loss approach which has of allowing limited modeling power to be solely devoted connections to the wake-sleep algorithm. Identifying to the task of interest, whereas the generative one can the joint or conditional distributions by only be extremely sensitive to faulty assumptions in the speech observing unpaired samples from the marginals is audio model despite the fact that this is not the primary only possible under certain conditions in the data object of interest. However the generative approach allows distribution and we discuss under what type of learning in a principled way from untranscribed speech conditional independence assumptions that might audio, something fundamentally impossible in the direct approach.

Deep Learning is the go-to technique for many applications, from natural language processing to biomedical. Deep learning can handle many different types of data such as images, texts, voice/sound, graphs and so on. This course will cover the basics of DL including how to build and train multilayer perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE) and generative adversarial networks (GANs). The course includes several hands-on projects, including cancer detection with CNNs, RNNs on disaster tweets, and generating dog images with GANs. Prior coding or scripting knowledge is required.

This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow. The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models. The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence models.

Traditional learning-based approaches to student modeling (e.g., predicting grades based on measured activities) generalize poorly to underrepresented/minority student groups due to biases in data availability. In this paper, we propose a Multi-Layer Personalized Federated Learning (MLPFL) methodology which optimizes inference accuracy over different layers of student grouping criteria, such as by course and by demographic subgroups within each course. In our approach, personalized models for individual student subgroups are derived from a global model, which is trained in a distributed fashion via meta-gradient updates that account for subgroup heterogeneity while preserving modeling commonalities that exist across the full dataset. To evaluate our methodology, we consider case studies of two popular downstream student modeling tasks, knowledge tracing and outcome prediction, which leverage multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums) in model training. Experiments on three real-world datasets from online courses demonstrate that our approach obtains substantial improvements over existing student modeling baselines in terms of increasing the average and decreasing the variance of prediction quality across different student subgroups. Visual analysis of the resulting students' knowledge state embeddings confirm that our personalization methodology extracts activity patterns which cluster into different student subgroups, consistent with the performance enhancements we obtain over the baselines.

Online Class Incremental learning (CIL) is a challenging setting in Continual Learning (CL), wherein data of new tasks arrive in incoming streams and online learning models need to handle incoming data streams without revisiting previous ones. Existing works used a single centroid adapted with incoming data streams to characterize a class. This approach possibly exposes limitations when the incoming data stream of a class is naturally multimodal. To address this issue, in this work, we first propose an online mixture model learning approach based on nice properties of the mature optimal transport theory (OT-MM). Specifically, the centroids and covariance matrices of the mixture model are adapted incrementally according to incoming data streams. The advantages are two-fold: (i) we can characterize more accurately complex data streams and (ii) by using centroids for each class produced by OT-MM, we can estimate the similarity of an unseen example to each class more reasonably when doing inference. Moreover, to combat the catastrophic forgetting in the CIL scenario, we further propose Dynamic Preservation. Particularly, after performing the dynamic preservation technique across data streams, the latent representations of the classes in the old and new tasks become more condensed themselves and more separate from each other. Together with a contraction feature extractor, this technique facilitates the model in mitigating the catastrophic forgetting. The experimental results on real-world datasets show that our proposed method can significantly outperform the current state-of-the-art baselines.