I’ve been working on Andrew Ng’s machine learning and deep learning specialization over the last 88 days. You will practice all these ideas in Python and in TensorFlow, which we will teach. (2016) This content is part of a series following the chapter 2 on linear algebra from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. Deep Learning is one of the most highly sought after skills in tech. less than 1 minute read. Coursera’s Deep Learning Specialization. Edit . You will work on case studies from healthcare, … Syllabus Course 1. 1. Posts go here See All … Deep Learning Specialization on Coursera Master Deep Learning, and Break into AI. Neural Networks and Deep Learning. Published: April 01, 2019. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. GANs Specialization. deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. If nothing happens, download GitHub Desktop and try again. Rather, I was taking this series of courses, con… We will help you become good at Deep Learning. Additional connection options Editing. The Deep Learning Specialization was created and is taught by Dr. Andrew Ng, a global leader in AI and co-founder of Coursera. If nothing happens, download the GitHub extension for Visual Studio and try again. Foundations of Deep Learning: Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper. I have a Ph.D. and am tenure track faculty at a top 10 CS department. Click to connect. These are my solutions for the exercises in the Deep Learning Specialization offered by Andrew Ng on Coursera. The Deep Learning Book - Goodfellow, I., Bengio, Y., and Courville, A. In addition to the lectures and programming assignments, you will also watch exclusive interviews with many Deep Learning leaders. Introduction to Deep Learning. You signed in with another tab or window. Learn more. Taught by Andrew Ng. The Deep Learning Specialization is designed to prepare learners to participate in the development of cutting-edge AI technology, and to understand the capability, the challenges, and the consequences of the rise of deep learning. — Andrew Ng, Founder of deeplearning.ai and Coursera Deep Learning Specialization, Course 5 Know how to apply convolutional networks to visual detection and recognition tasks. Deep Learning The Deep Learning Specialization is designed to prepare learners to participate in the development of cutting-edge AI technology, and to understand the capability, the challenges, and the consequences of the rise of deep learning. Done and pass 100% all Quiz and Programming Assignments. So, your mileage may vary. Deep Learning Specialization by deeplearning.ai on Coursera. File . Understand how to build a convolutional neural network, including recent variations such as residual networks. This is my summary of learning Deep Learning Specialization on Coursera, which consists of 5 courses as following:. Understand industry best-practices for building deep learning applications. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will master not only the theory, but also see how it is applied in industry. You signed in with another tab or window. Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. GANs Specialization made by deeplearning.ai (Generative Adversarial Networks Specialization) This 3-course specialization is launched on September 30. This repo contains all my work for this specialization. Please only use it as a reference. Share. But, first: I’m probably not the intended audience for the specialization. Highly recommend anyone wanting to break into AI. Building your Deep Neural Network - Step by Step In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Week 2. The course appears to be geared towards people with a computing background who want to get an industry job in “Deep Learning”. If you want to break into AI, this Specialization will help you do so. How long is the course? This repository contains my full work and notes on upcoming Deeplearning.ai GAN Specialization the GAN specialization has two courses which can be taken on Coursera. Runtime . Sign in. GitHub; Kaggle; Posts; Twitter; 1 min read deeplearning.ai Specialization 2019/12/18. Can’t wait to apply some of the idea in my research work. I created this repository post completing the Deep Learning Specialization on coursera. ⚡ Develop Machine Learning/Deep Learning Solutions (using python, R, Cloud services) ⚡ Applying technology for better understanding and prediction in improving business functions and growth profitability ⚡ Deployment of ML/Dl models on third party services such as heroku/ AWS / GCP ⚡ Integration and Automation testing with Circle CI. Deep Learning Specialization. The course covers deep learning from begginer level to advanced. If nothing happens, download Xcode and try again. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Copy to Drive Connect RAM. Open settings. Done and pass 100% all Quiz and Programming Assignments. More advanced learners can go deep and go fundamentals such as the theory of deep learning https://stats385.github.io/ and understand how masters of the master … Cheers! Deep Learning is one of the most highly sought after skills in tech. Instructor: Andrew Ng, DeepLearning.ai. Deep Learning is a superpower.With it you can make a computer see, synthesize novel art, translate languages, render a medical diagnosis, or build pieces of a car that can drive itself.If that isn’t a superpower, I don’t know what is. It aims to provide intuitions/drawings/python code on mathematical theories and is constructed as my understanding of these concepts. Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. Neural Networks and Deep Learning. I am really glad if you can use it as a reference and happy to discuss with you about issues related with the course even further deep learning techniques. Work fast with our official CLI. Quiz 1 This is the repository for my implementations on the Deep Learning Specialization from Coursera. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. deepanshut041.github.io/deep-learning-specialization/, download the GitHub extension for Visual Studio, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. Use Git or checkout with SVN using the web URL. Disk. Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. Machine Learning (Left) and Deep Learning (Right) Overview. Be able to apply sequence models to natural language problems, including text synthesis. I am not that. The quiz and assignments are relatively easy to answer, hope you can have fun with the courses. This is my personal projects for the course. Coursera Deep Learning Specialization View on GitHub Deep Learning. These are my solutions for the exercises in the Introduction to Deep Learning course that is part of the Advanced Machine Learning Specialization on Coursera. Neural Networks and Deep Learning. Deep Learning Specialization on Coursera. The prefilled assignment files are already completed. This is the repository for my implementations on the Deep Learning Specialization from Coursera. This is the fifth and final course of the Deep Learning Specialization. Neural Network and Deep Learning. Instructor: Andrew Ng Community: deeplearning.ai Overview. Logistic Regression with a Neural Network mindset; Week 3. Use Git or checkout with SVN using the web URL. Coursera - Deep Learning Specialization - Course3.ipynb_ Rename. They will share with you their personal stories and give you career advice. Code. Insert . Planar data classification with one hidden layer; Week 4. Courses on Coursera All Videos. Understand how to diagnose errors in a machine learning system, and, Be able to prioritize the most promising directions for reducing error, Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance, Know how to apply end-to-end learning, transfer learning, and multi-task learning. The repository contains files for Course 1, 2, 3. English. deeplearning.ai. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content. The two courses are: Offered by DeepLearning.AI. Date Issued: May 29, 2019 Credential ID: 9NFXTK8S5DEH. Help . Master Deep Learning, and Break into AI. deeplearning.ai / Coursera. Some of my Medium Post. Neural Networks and Deep Learning (Certificate) Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization (Certificate) Structuring Machine Learning Projects (Certificate) Deep Learning Specialization. If nothing happens, download Xcode and try again. Contribute to DoDuy/Deep-Learning-Specialization development by creating an account on GitHub. 1st course: Neural Networks and Deep Learning 2nd course: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization 3rd course: Structuring Machine Learning Projects 4th course: Convolutional Neural Networks Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance. Work fast with our official CLI. Add text cell. Week 1. I enjoyed this course a lot. If nothing happens, download GitHub Desktop and try again. Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking. • Deep Learning View My GitHub Profile spmielke@gmail.com Deep Learning Specialization. Over the next few days, I’ll go over (this time I am paying and thus have access to the exams :)) the deeplearning.ai Coursera Specialization. Deep Learning. I was not getting this certification to advance my career or break into the field. We will help you become good at Deep Learning. Deep Learning Specialization. Tools . Neural Networks and Deep Learning by deeplearning.ai on Coursera. You will also explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. Be able to apply sequence models to audio applications, including speech recognition and music synthesis. If you want to break into AI, this Specialization will help you do so. Be able to implement a neural network in TensorFlow. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization by deeplearning.ai on Coursera. Instructor, Alama Initiative, Egypt, 2018 I volunteered to teach deep learning concepts for a group of 20 undergrad and grad students taking Coursera’s deep-learning specialization following up with their progress throughout the courses. Contribute to DoDuy/Deep-Learning-Specialization development by creating an account on GitHub. Edits. Github; Google Scholar; Deep Learning Specialization. Its includes solutions to the quizzes and programming assignments which are required for successful completion of the courses. Share notebook. Recently I have completed the 5-month journey of the Deep Learning specialization on Coursera. Ctrl+M B. Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework. Deep Learning Specialization on Coursera. If nothing happens, download the GitHub extension for Visual Studio and try again. Deep learning is a powerful application of machine learning (ML) algorithms modeled after biological systems of information processing called artificial neural networks (ANN). Learn more. Toggle header visibility . on Coursera, by National Research University Higher School of Economics. All the code base, images etc have been taken from the specialization, unless specified otherwise. View . Deep Learning Specialization courses by Andrew Ng, deeplearning.ai - AdalbertoCq/Deep-Learning-Specialization-Coursera Here, I’ll gather my notes of the course for easy access: Neural Networks and Deep Learning; Improving Deep Neural Networks: Hyperparameter … Deeplearning.ai Generative Adversarial Networks Specialization. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Know to use neural style transfer to generate art. If nothing happens, download GitHub Desktop and try again. (2016). download the GitHub extension for Visual Studio, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Initialization, Regularization & Gradient Check, Hyperparameter tuning, Batch Normalization & Tensorflow Implementation, Convolutional Neural Network Implementation in Numpy, Deep Residual Learning for Image Recognition, You Only Look Once: Unified, Real-Time Object Detection, FaceNet: A Unified Embedding for Face Recognition and Clustering, Going deeper with convolutions (Inception Networks), RNN & LSTM Implementation in Numpy (Including backpropagation), Natural Language Processing & Word Embeddings, Neural Machine Translation with Attention, Understand the major technology trends driving Deep Learning, Be able to build, train and apply fully connected deep neural networks, Know how to implement efficient (vectorized) neural networks, Understand the key parameters in a neural network's architecture. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Insert code cell below. Course 1. Text. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Deep Learning Specialization on Coursera. Which are required for successful completion of the Deep Learning Specialization from Coursera variations such as GRUs and LSTMs i. Learning, and more from the Specialization are required for successful completion of the courses become good at Learning. To generate art pass 100 % all Quiz and programming assignments Learning View my Profile. These concepts been taken from the Specialization is taught by two experts in NLP, machine framework! Solutions for the Deep Learning Specialization View on GitHub GitHub Desktop and again! Applied in industry my summary of Learning Deep Learning Specialization was created and is taught by Dr. Ng! 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Theory, but also See how it is applied in industry of how to set up train/dev/test sets analyze! Language problems, including text synthesis deep learning specialization github and Deep Learning Specialization over the last days... ’ t wait to apply sequence models to audio applications, including text synthesis but, first: i m.