Neural Networks: Instructional Materials


Top 10 Videos on Deep Learning in Python

@machinelearnbot

This 'Top 10' list has been created on the basis of best content, and not exactly the number of views. To help you choose an appropriate framework, we first start with a video that compares few of the popular Python DL libraries. I have included the highlights and my views on the pros and cons of ea...


5 Fantastic Practical Natural Language Processing Resources

#artificialintelligence

Are you interested in some practical natural language processing resources? There are so many NLP resources available online, especially those relying on deep learning approaches, that sifting through to find the quality can be quite a task. There are some well-known, top notch mainstay resources o...


Natural Language Processing with Deep Learning in Python

@machinelearnbot

In this course we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to...


Unsupervised Deep Learning in Python Udemy

@machinelearnbot

This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these...


Convolutional Neural Networks For All Part II – Machine Learning World – Medium

#artificialintelligence

If you're not a Deep Learning expert, chances are that the Coursera Convolutional Neural Networks course kicked your behind. So much information, so many complex theories covered in such a short time! Countless times pausing the lectures, rereading additional material and discussing topics later led us, a group of official mentors, to decide a learner study guide is worth the effort. Part I reviews the broad concepts covered in this course. Part II summarizes every single lecture for you. Part III will offer a deeplearning.ai dictionary to help you sort through the jungle of acronyms, technical terms and occasional jokes from grandmaster Ng once we've finished course 5. Let's dive deeper into the bewilderment of interesting information by recapping every lecture.



Neural Networks for Machine Learning Coursera

@machinelearnbot

Very good overview of NN, and met all my expectations. What I hoped for was to gain an overall understanding of different NN approaches and understand how to design a NN model architecture. However, some small critiques:


The Unconventional Guide To The Best Websites For Quants

#artificialintelligence

Technology moves at a startling speed and it has been the same case in the algorithmic and quantitative trading domain. Traders around the world are making use of Machine Learning, Artificial Intelligence, Blockchain, Neural Networks, Deep Learning and similar techniques to execute their trades. One...


Become a Deep Learning Coder From Scratch in Under a Year

#artificialintelligence

Machine learning (aka A.I.) seems bizarre and complicated. It's the tech behind image and speech recognition, recommendation systems, and all kinds of tasks that computers used to be really bad at but are now really good at. It involves teaching a computer to teach itself. And you can learn to do it in well under a year, according to data scientist Bargava. You'll need to put in a solid 10-20 hours a week, but you will learn a lot along the way. When an app claims to be powered by "artificial intelligence" it feels like you're in the future.… Bargava lays out a six-month, five-step plan for coders to learn about deep learning. If you don't already know how to code, that's step zero. Try the free course at LearnPython.org, or shell out $200 for Codecademy's 10-week Python course. Then follow Bargava's steps, which include more online courses, some solo projects, and some extra reading. It might be grueling, but Bargava says that if you stick to it, by the end you'll be able to "learn, follow and contribute to state-of-art work in deep learning."


Convolutional Neural Networks For All Part I – Towards Data Science

#artificialintelligence

The first three courses of the Coursera Deep Learning Specialization were bearably tough, but then came course 4. So many great topics and concepts! But countless times stopping the videos, note taking, and lecture rewatching led us, a group of official mentors, to decide a learner study guide is worth the effort. Part I of this study guide trilogy reviews the broad concepts covered in this course. What are Convolutional Neural Networks and how does YOLO actually work? Part II summarizes every single lecture and dives deeper into explaining the top-level concepts. Part III will offer a deeplearning.ai dictionary to help you sort through the jungle of acronyms, technical terms and occasional jokes from grandmaster Ng and will be published once we've finished course 5. Let's start by breaking down the most interesting concepts of the CNN course one by one.