Welcome to the course "Python: Solved Interview Questions on Algorithms and Data structures". We would have observed the fact that though most of us are developers, only few would get a chance to work on certain advanced programming stuff like Data Structures, Linked Lists, Trees. The rest of us get to spend time in Bug fixing, resolving Maintenance issues during our work hours. Though this work doesn't help us much in improving our learning curve, it certainly feeds us and our families. So, Keeping this in mind, at the work place, We don't have any option but to work honestly.
About this course: This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. This is the fifth and final course of the Deep Learning Specialization. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.
The inputs were 96x96 images, and the target outputs were 30-value vectors indicating x,y pairs for 15 facial keypoints. We had to design a CNN from scratch to perform the task. My architecture was three convolutional layers, each followed by a max pooling layer with dropout, then a two-layer dense regression network at the end. Training was done on an EC2 p2xlarge GPU instance, and took around 10 minutes to perform 250 epochs (though there was a lot of trial and error so all told I spent a few hours on training different architectures). The dataset came from this Kaggle competition!
Artificial Intelligence is one of the hottest fields in computer science right now and has taken the world by storm as a major field of research and development. Python has surfaced as a dominant language in AI/ML programming because of its simplicity and flexibility, as well as its great support for open source libraries such as Scikit-learn, Keras, spaCy, and TensorFlow. If you're a Python developer who wants to take first steps in the world of artificial intelligent solutions using easy-to-follow projects, then go for this learning path. This comprehensive 2-in-1 course is designed to teach you the fundamentals of deep learning and use them to build intelligent systems. You will solve real-world problems such as face detection, handwriting recognition, and more.
The Explainable Machine Learning Challenge is a collaboration between Google, FICO and academics at Berkeley, Oxford, Imperial, UC Irvine and MIT, to generate new research in the area of algorithmic explainability. Teams will be challenged to create machine learning models with both high accuracy and explainability; they will use a real-world financial dataset provided by FICO. Designers and end users of machine learning algorithms will both benefit from more interpretable and explainable algorithms. Machine learning model designers will benefit from Model explanations, written explanations describing the functioning of a trained model. These might include information about which variables or examples are particularly important, they might explain the logic used by an algorithm, and/or characterize input/output relationships between variables and predictions.
My initial uncharitable take was: College profs need to understand product development is harder than they think. A number of points here indicate a lack of progress on general AI by pointing to product decisions that would be present no matter the implementation. He seems to then use this as a rallying call to encourage going back to knowledge engineering circa late 80's early 90's? I'm going to guess i'm missing something. Yes general ai is far away.
This is the new book by Andrew Ng, still in progress. Andrew Yan-Tak Ng is a computer scientist and entrepreneur. He is one of the most influential minds in Artificial Intelligence and Deep Learning. Ng founded and led Google Brain and was a former VP & Chief Scientist at Baidu, building the company's Artificial Intelligence Group into several thousand people. He is an adjunct professor (formerly associate professor and Director of the AI Lab) at Stanford University.
"This course has taught me many things I wanted to know about pandas. It covers everything since the installation steps, so it is very good for anyone willing to learn about data analysis in python /jupyter environment." "Good explanation, I have laready used two online tutorials on data -science and this one is more step by step, but it is good" "i have studied python from other sources as well but here i found it more basic and easy to grab especially for the beginners. I can say its best course till now . "The instructor is so good, he helps you in all doubts within an average replying time of one hour.
Figure Eight can help you train, test, and tune your machine learning models, but building a strategy for your AI infrastructure can be challenging –– especially for compute-intensive deep learning workloads. That's why I&O leaders must choose the right accelerators for devising effective deep learning compute infrastructure strategies. Download the report Find the Right Accelerator for your Deep Learning Needs to learn how I&O leaders must deliver effective machine learning infrastructures that effectively balance performance, cost, and functionality while minimizing complexity.