It goes without saying that there are a nearly unlimited number of resources available for learning deep learning from scratch. This applies at least equally to freely-available resources, it not more so than paid. A course which has been on the community's radar recently and being shared widely across social media is the aptly titled Deep Learning course from the NYU Center for Data Science, taught by Yann LeCun & Alfredo Canziani. This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.

Explainable artificial intelligence is an emerging method for boosting reliability, accountability, and dependence in critical areas. This is done by merging machine learning approaches with explanatory methods that reveal what the decision criteria are or why they have been established and allow people to better understand and control AI-powered tools. Below here, we have discussed some of the important milestones, in no particular order, on explainable AI (XAI) in 2020. Fairlearn is a popular explainable AI toolkit that enables data scientists as well as developers to evaluate and enhance the fairness of their AI systems. The toolkit has two components, an interactive visualisation dashboard and unfairness mitigation algorithms.

AI Policy Matters is a regular column in the ACM SIGAI AI Matters newsletter featuring summaries and commentary based on postings that appear twice a month in the AI Matters blog. Confusion in the popular media about terms such as algorithm and what constitutes AI technology cause critical misunderstandings among the public and policymakers. More importantly, the role of data is often ignored in ethical and operational considerations. Even if AI systems are perfectly built, low quality and biased data cause unintentional and even intentional hazards. A generative pre-trained transformer GPT-3 is currently in the news.

The biggest problem which restricts someone to understand the concepts of Data Science is the wrong approach towards learning & understanding it. This new approach of learning data science is completely wrong, & according to me it is the worst approach, but the sad reality is that most of the people are following this wrong/worst approach only. One hype is created in the mindset of the majority of the people, & the hype is that at the majority of the places/situations/scenarios Deep Learning helps, then at few situations, Machine Learning helps, & at the last, at very few situations, Statistics helps. This hype/mindset is completely wrong & the reality is exactly opposite to this hype. In reality, Deep Learning is the subset of the Machine Learning, & Machine Learning itself is completely dependent on Statistics.

Despite the challenges of 2020, the AI research community produced a number of meaningful technical breakthroughs. GPT-3 by OpenAI may be the most famous, but there are definitely many other research papers worth your attention. For example, teams from Google introduced a revolutionary chatbot, Meena, and EfficientDet object detectors in image recognition. Researchers from Yale introduced a novel AdaBelief optimizer that combines many benefits of existing optimization methods. OpenAI researchers demonstrated how deep reinforcement learning techniques can achieve superhuman performance in Dota 2. To help you catch up on essential reading, we've summarized 10 important machine learning research papers from 2020. These papers will give you a broad overview of AI research advancements this year.

We always heard that Neural Networks (NNs)are inspired by biological neural networks. This huge representation was done in a fantastic way. Figure 1 shows the anatomy of a single neuron. The central part is called the cell body where the nucleus resides. There are various wires which pass the stimulus to the cell body and few wires which send the output to the other neurons. The thickness of the dendrites implies the weight/bias/power of the stimulus.

Today the UK natural intelligence company Opteran has raised around €2.3 million in seed funding to pioneer its lightweight, silicon-based approach to autonomy, created by testing insect brains, in what to some would sound a little like a Black Mirror episode. Opteran is a University of Sheffield spin-out based on eight years of research by Professor James Marshall and Dr. Alex Cope into insect brains as part of the Green Brain and Brains on Board projects. Although insects have smaller brains, they are still capable of sophisticated decision making and navigation using optic flow to perceive depth and distance. The Opteran team state that this is a far more efficient, robust and transparent way to achieve autonomy than current deep learning techniques, enabling the team to reverse-engineer insect brains to produce algorithms requiring no data centre or extensive pre-training. It means Opteran can mimic tasks such as seeing, sensing objects, obstacle avoidance, navigation and decision making.

Training machine learning/deep learning models can take a really long time, and understanding what is happening as your model is training is absolutely crucial. Depending on the library or framework, this can be easier or more difficult, but pretty much always it is doable. Let me show how to monitor machine learning models in each case. Some frameworks, especially lower-level ones, don't have an elaborate callback system in place, and you have direct access to the training loop. One such framework example is PyTorch.

We recommend these YouTube channels regardless of your machine learning experience, whether you have a computer science degree or just a passing interest in AI. You'll soon be on the way toward mastering the basics of AI, machine learning, and computer science in no time, through easy-to-follow demos and tutorial videos. The official Deep Learning AI YouTube channel has video tutorials from the deep learning specialization on Coursera. Artificial Intelligence -- All in One: This YouTube channel has tutorial videos related to science, technology, and artificial intelligence. Andrew Ng: Andrew Ng is a computer scientist and entrepreneur, co-founder of Google Brain, former VP & Chief Scientist at Baidu, adjunct professor at Stanford University.

As deep learning has grown in popularity over the last two decades, more and more companies and developers have created frameworks to make deep learning more accessible. Now there are so many deep learning frameworks available that the average deep learning practitioner probably isn't even aware of all of them. With so many options available, which framework should you pick? In this article, I will give you a tour of some of the most common Python deep learning frameworks and compare them in a way that allows you to decide which framework is the right one to use in your projects. I have purposely bundled these two frameworks together because the latest versions of TensorFlow are tightly integrated with Keras.