Notes from Coursera Deep Learning courses by Andrew Ng

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Tess Ferrandez

My notes from the excellent Coursera specialization by Andrew Ng

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Notes from Coursera Deep Learning courses by Andrew Ng

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This Naive Bayes Classifier tutorial presentation will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this presentation, you will also implement Naive Bayes algorithm for text classification in Python. The topics covered in this Naive Bayes presentation are as follows: 1. What is Naive Bayes? 2. Naive Bayes and Machine Learning 3. Why do we need Naive Bayes? 4. Understanding Naive Bayes Classifier 5. Advantages of Naive Bayes Classifier 6. Demo - Text Classification using Naive Bayes - - - - - - - - Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. - - - - - - - -

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(Python Certification Training for Data Science: https://www.edureka.co/python) This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics: 1. Machine learning Overview 2. Introduction to Scikit-learn 3. Installation of Scikit-learn 4. Regression and Classification 5. Demo Subscribe to our channel to get video updates. Hit the subscribe button and click the bell icon.

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Computer Vision 분야에서 CNN은 과연 살아남을 수 있을까요? 안녕하세요 TensorFlow Korea 논문 읽기 모임 PR-12의 317번째 논문 리뷰입니다. 이번에는 Google Research, Brain Team의 MLP-Mixer: An all-MLP Architecture for Vision을 리뷰해보았습니다. Attention의 공격도 버거운데 이번에는 MLP(Multi-Layer Perceptron)의 공격입니다. MLP만을 사용해서 Image Classification을 하는데 성능도 좋고 속도도 빠르고.... 구조를 간단히 소개해드리면 ViT(Vision Transformer)의 self-attention 부분을 MLP로 변경하였습니다. MLP block 2개를 사용하여 하나는 patch(token)들 간의 연산을 하는데 사용하고, 하나는 patch 내부 연산을 하는데 사용합니다. 사실 MLP를 사용하긴 했지만 논문에도 언급되어 있듯이, 이 부분을 일종의 convolution이라고 볼 수 있는데요... 그래도 transformer 기반의 network이 가질 수밖에 없는 quadratic complexity를 linear로 낮춰주고 convolution의 inductive bias 거의 없이 아주아주 simple한 구조를 활용하여 이렇게 좋은 성능을 보여준 점이 멋집니다. 반면에 역시나 data를 많이 써야 한다거나, MLP의 한계인 fixed length의 input만 받을 수 있다는 점은 단점이라고 생각하는데요, 이 연구를 시작으로 MLP도 다시한번 조명받는 계기가 되면 좋을 것 같네요 비슷한 시점에 나온 비슷한 연구들도 마지막에 간략하게 소개하였습니다. 재미있게 봐주세요. 감사합니다! 논문링크: https://arxiv.org/abs/2105.01601 영상링크: https://youtu.be/KQmZlxdnnuY

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This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters. Below topics are explained in this Machine Learning presentation: 1. Why Machine Learning? 2. What is Machine Learning? 3. Types of Machine Learning 4. Machine Learning Algorithms - Linear Regression - Decision Trees - Support Vector Machine 5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/

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The first lecture from the Machine Learning course series of lectures. The lecture covers basic principles of machine learning, such as the difference between supervised and unsupervised learning, several classifiers: nearest neighbour (k-NN), decision trees, random forest, major obstacles in machine learning: overfitting and the curse of dimensionality, followed by cross-validation algorithm and general ML pipeline. A link to my github (https://github.com/skyfallen/MachineLearningPracticals) with practicals that I have designed for this course in both R and Python. I can share keynote files, contact me via e-mail: dmytro.fishman@ut.ee.

1 Supervised learning

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This presentation Neural Network will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a use case implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into these slides to understand how a neural network actually work. Below topics are explained in this neural network presentation: 1. What is Neural Network? 2. What can Neural Network do? 3. How does Neural Network work? 4. Types of Neural Network 5. Use case - To classify between the photos of dogs and cats Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Learn more at: https://www.simplilearn.com

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