The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.

The "black-box" conundrum is one of the biggest roadblocks preventing banks from executing their artificial intelligence (AI) strategies. It's easy to see why: Picture a large bank known for its technology prowess designing a new neural network model that predicts creditworthiness among the underserved community more accurately than any other algorithm in the marketplace. This model processes dozens of variables as inputs, including never-before-used alternative data. The developers are thrilled, senior management is happy that they can expand their services to the underserved market, and business executives believe they now have a competitive differentiator. But there is one pesky problem: The developers who built the model cannot explain how it arrives at the credit outcomes, let alone identify which factors had the biggest influence on them.

Natural Language Processing (NLP) has long played a significant role in the compliance processes for major banks around the world. By implementing the different NLP techniques into the production processes, compliance departments can maintain detailed checks and keep up with regulator demands. All of these areas can benefit from document processing and the use of NLP techniques to get through the process more effectively. Certain verification tasks fall beyond the realm of using traditional, rules-based NLP systems. This is where deep learning can help fill these gaps, providing smoother and more efficient compliance checks. There are several challenges that make the rules-based system more complicated to use when undergoing check routines.

Are you looking for the Best Certification Courses for Artificial Intelligence?. If yes, then your search will end after reading this article. In this article, I will discuss the 10 Best Certification Courses for Artificial Intelligence. So, give your few minutes to this article and find out the Best AI Certification Course for you. Artificial Intelligence is changing our lives.

This article was published as a part of the Data Science Blogathon. A few days ago, I came across a question on "Quora" that boiled down to: "How can I learn Natural Language Processing in just only four months?".

What do you achieve with deep learning? Deep learning is a part of our daily life. For example, when you upload a photo to Facebook, deep learning helps by automatically tagging your friends. If you use digital assistants like Siri, Cortana or Alexa, they serve you to the benefit with the help of natural language processing and speech recognition. When you meet with overseas customers on Skype, it translates in real time.

Stephanie Condon is a senior staff writer for Red Ventures based in Portland, Oregon, covering business technology for ZDNet. Intel on Tuesday launched the latest generation of its deep learning processors for training and inference, Habana Gaudi2 and Habana Greco, making AI more accessible and valuable for its data center customers. At its Intel Vision event, the chipmaker also shared details about its IPU and GPU portfolios, all aimed business customers. "AI is driving the data center," Eitan Medina, COO of Habana Labs, Intel's data center team focused on AI deep learning processor technologies, said to reporters earlier. But different customers are using different mixes for different applications."

Conversational artificial intelligence (CAI) uses deep learning (DL), a subset of machine learning (ML), to automate speech recognition, natural language processing and text to speech using machines.

Humans possess a powerful ability to reason. They understand a question asked by a fellow human-being and provide the most appropriate answer to it. A human brain can do quick mathematics to answer a trivial question like "If I have 10 balls and bought two cans, each having 5 balls, how many balls would I have?" The humans can do commonsense reasoning like "If a driver sees a pedestrian on the crossover, what would he do?" Humans have intelligence in understanding if somebody is cutting a joke and probably get a deeper understanding of what the speaker really wants to say? The question is, can we train the machines to gain this kind of intelligence that we humans possess?

Today, we look at the basics of artificial intelligence, which permeates almost every aspect of our lives. This article will explore the main concepts revolving around artificial intelligence and the answers to frequently asked questions without getting into technical complexities as much as possible. Artificial intelligence (AI) is a field of computer science that focuses on developing smart machines capable of accomplishing tasks that require human intellect. Most people immediately think of Artificial General Intelligence (AGI) when they hear about AI. It can perform anything that a human being can, but it does so far superior. However, the fact is that we are nowhere near to creating one.