"Data Science" and "Data Analytics" are the most booming fields today and a large number of the crowd is inclined towards learning the same. Starting a career in Data Science and working in this field may not be as easy as it seems in the courses that are available online. You need to have a firm hold on critical thinking and analysis and need to pay great attention to details while solving any problem. Along with solving assignments and exploring the field you might as well get paid for it! So how to start freelancing in Data Science?

Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation of gradient descent is that a single step size (learning rate) is used for all input variables. Extensions to gradient descent, like the Adaptive Movement Estimation (Adam) algorithm, use a separate step size for each input variable but may result in a step size that rapidly decreases to very small values. AdaMax is an extension to the Adam version of gradient descent that generalizes the approach to the infinite norm (max) and may result in a more effective optimization on some problems. In this tutorial, you will discover how to develop gradient descent optimization with AdaMax from scratch.

In this article, you will learn Machine Learning (ML) model deployment using Django. We will also discuss the ML Problem Statement which is HR Analytics. I have taken this problem from Analytics Vidhya. A special thank you to them for providing such amazing problem statements. Now before we start, take a look at this website-HR Analytics.

Northeast Wisconsin Technical College is hosting an Artificial Intelligence Bootcamp in partnership with the Mark Cuban Foundation. Cuban, an American billionaire entrepreneur and television personality, created an AI bootcamp offered in select cities across the county to train the next generation of AI leaders. NWTC is the only college in the country to be part of this bootcamp. During the course of the week, high school students will learn about AI, its ethical implications and where they already interact with it in their own lives. "I didn't know any of this stuff even existed in life," said Kennedy Raboin, a student participant.

The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the deployment of fully supervised algorithms unfeasible. On the other hand, the problem of semi-supervised large-scale data streams is little explored in the literature because most works are designed in the traditional single-node computing environments while also being fully supervised approaches. This paper offers Weakly Supervised Scalable Teacher Forcing Network (WeScatterNet) to cope with the scarcity of labelled samples and the large-scale data streams simultaneously. WeScatterNet is crafted under distributed computing platform of Apache Spark with a data-free model fusion strategy for model compression after parallel computing stage. It features an open network structure to address the global and local drift problems while integrating a data augmentation, annotation and auto-correction ($DA^3$) method for handling partially labelled data streams. The performance of WeScatterNet is numerically evaluated in the six large-scale data stream problems with only $25\%$ label proportions. It shows highly competitive performance even if compared with fully supervised learners with $100\%$ label proportions.

Breakthroughs in machine learning in the last decade have led to `digital intelligence', i.e. machine learning models capable of learning from vast amounts of labeled data to perform several digital tasks such as speech recognition, face recognition, machine translation and so on. The goal of this thesis is to make progress towards designing algorithms capable of `physical intelligence', i.e. building intelligent autonomous navigation agents capable of learning to perform complex navigation tasks in the physical world involving visual perception, natural language understanding, reasoning, planning, and sequential decision making. Despite several advances in classical navigation methods in the last few decades, current navigation agents struggle at long-term semantic navigation tasks. In the first part of the thesis, we discuss our work on short-term navigation using end-to-end reinforcement learning to tackle challenges such as obstacle avoidance, semantic perception, language grounding, and reasoning. In the second part, we present a new class of navigation methods based on modular learning and structured explicit map representations, which leverage the strengths of both classical and end-to-end learning methods, to tackle long-term navigation tasks. We show that these methods are able to effectively tackle challenges such as localization, mapping, long-term planning, exploration and learning semantic priors. These modular learning methods are capable of long-term spatial and semantic understanding and achieve state-of-the-art results on various navigation tasks.

Understanding machine learning and deep learning concepts is essential, but if you're looking to build an effective AI career, you need production engineering capabilities as well. Earlier last month, DeepLearning.AI launched a much anticipated Machine Learning Engineering for Production (MLOps) Specialization. It covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this efficiently, as well as important concepts in the emerging fields of MLOps and data-centric AI. To celebrate the launch of the new program, we are pleased to invite you to join us on June 30 for our live virtual event where our instructors for the MLEP Specialization are joined with industry speakers to talk about machine learning engineering for production, as well as a sneak peek of the MLEP Specialization.

AI can point out places where courses need to improve. Identifying the gaps in educational programs ensures that learning institutions do more to prepare students for the job market. Now, even online course providers are using AI to improve their offerings. An excellent way of determining that a course needs to be more extensive is when a large number of students always seem to submit the wrong answer to specific homework assignments. If a learning institution has an AI system in place, it will notify the teachers about this problem.

This course will teach you foundations of deep learning and TensorFlow as well as prepare you to pass the TensorFlow Developer Certification exam (optional). Videos going through the rest of the notebooks (03 - 10) are available in the full course. New You can now read the full course as an online book! (note: this is a work in progress, but 95% of it should run fine) Check out the livestream Q&A celebrating the course launch on YouTube. Otherwise, many of them might be answered below. This table is the ground truth for course materials.

According to a 2020 report by Emergence, 80% of the global workforce does not sit behind a desk. According to a 2020 report by Emergence, 80% of the global workforce does not sit behind a desk. That’s an overwhelming majority of workers who are deskless and increasingly reliant on technology to do their jobs in industries impacted by factors like the growing skills gap and, most recently, a global pandemic. While employers have done much to address the needs of deskless workers over the past year, there’s untapped opportunity to make these workers – and, in turn, the industries they support – more efficient, resilient, and safe in the current working environment and beyond. On Wednesday, June 23, Rolls Royce’s XXX will join industry experts YYY from PwC and ZZZ from Librestream to teach enterprises about the power of Augmented Reality (AR) and Artificial Intelligence (AI) to enable deskless workers around the world and build knowledge networks capable of sustaining the deskless workforce for decades to come. In this webinar, you will learn: Why traditional deskless worker solutions have fallen short at a time when effective remote collaboration is of peak importance How AR plus AI can improve knowledge sharing among distributed workforces, reduce knowledge loss, eliminate inefficiencies, enhance safety, improve sustainability, lower costs, and more Real-world use cases of AR and AI on devices like Microsoft’s HoloLens and the generated ROI Why organizations with large deskless workforces prefer solutions like Librestream’s AI Connected Expert Vision: Broad device support, specialized accessories, etc. Realizing true IoT: Where AI and AR converge to create the fully connected, deskless worker of tomorrow