You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Planar data classification with one hidden layer; Week 4. Recently I have completed the 5-month journey of the Deep Learning specialization on Coursera. Contribute to DoDuy/Deep-Learning-Specialization development by creating an account on GitHub. Machine Learning (Left) and Deep Learning (Right) Overview. Building your Deep Neural Network - Step by Step Work fast with our official CLI. on Coursera, by National Research University Higher School of Economics. You will master not only the theory, but also see how it is applied in industry. GitHub; Kaggle; Posts; Twitter; 1 min read deeplearning.ai Specialization 2019/12/18. Share notebook. The course covers deep learning from begginer level to advanced. 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. Coursera Deep Learning Specialization View on GitHub Deep Learning. Ctrl+M B. It aims to provide intuitions/drawings/python code on mathematical theories and is constructed as my understanding of these concepts. This is the fifth and final course of the Deep Learning Specialization. We will help you become good at Deep Learning. 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. This is the repository for my implementations on the Deep Learning Specialization from Coursera. If nothing happens, download Xcode and try again. Neural Networks and Deep Learning. I enjoyed this course a lot. Week 1. Know how to apply convolutional networks to visual detection and recognition tasks. 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. ⚡ 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. If you want to break into AI, this Specialization will help you do so. Deep learning is a powerful application of machine learning (ML) algorithms modeled after biological systems of information processing called artificial neural networks (ANN). 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. (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. Work fast with our official CLI. Courses on Coursera All Videos. The Deep Learning Book - Goodfellow, I., Bengio, Y., and Courville, A. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization by deeplearning.ai on Coursera. Learn more. Be able to apply sequence models to audio applications, including speech recognition and music synthesis. Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework. Date Issued: May 29, 2019 Credential ID: 9NFXTK8S5DEH. Deep Learning Specialization. You signed in with another tab or window. Here, I’ll gather my notes of the course for easy access: Neural Networks and Deep Learning; Improving Deep Neural Networks: Hyperparameter … 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. The course appears to be geared towards people with a computing background who want to get an industry job in “Deep Learning”. Highly recommend anyone wanting to break into AI. Rather, I was taking this series of courses, con… 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. Deep Learning. Understand how to build a convolutional neural network, including recent variations such as residual networks. deeplearning.ai / Coursera. If nothing happens, download GitHub Desktop and try again. 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. Master Deep Learning, and Break into AI. Deep Learning is one of the most highly sought after skills in tech. Copy to Drive Connect RAM. Be able to apply sequence models to natural language problems, including text synthesis. Use Git or checkout with SVN using the web URL. Week 2. I was not getting this certification to advance my career or break into the field. Deep Learning Specialization on Coursera Master Deep Learning, and Break into AI. Code. Coursera - Deep Learning Specialization - Course3.ipynb_ Rename. 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. Its includes solutions to the quizzes and programming assignments which are required for successful completion of the courses. Learn more. Tools . 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. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. If nothing happens, download the GitHub extension for Visual Studio and try again. The two courses are: Github; Google Scholar; Deep Learning Specialization. Deep Learning Specialization by deeplearning.ai on Coursera. 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. View . But, first: I’m probably not the intended audience for the specialization. They will share with you their personal stories and give you career advice. Offered by DeepLearning.AI. Runtime . Deep Learning Specialization. Syllabus Course 1. File . Use Git or checkout with SVN using the web URL. We will help you become good at Deep Learning. 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 effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking. You will practice all these ideas in Python and in TensorFlow, which we will teach. You will work on case studies from healthcare, … If you want to break into AI, this Specialization will help you do so. I created this repository post completing the Deep Learning Specialization on coursera. Understand industry best-practices for building deep learning applications. The Deep Learning Specialization was created and is taught by Dr. Andrew Ng, a global leader in AI and co-founder of Coursera. Neural Networks and Deep Learning (Certificate) Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization (Certificate) Structuring Machine Learning Projects (Certificate) In addition to the lectures and programming assignments, you will also watch exclusive interviews with many Deep Learning leaders. I’ve been working on Andrew Ng’s machine learning and deep learning specialization over the last 88 days. Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. 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. Insert code cell below. Neural Networks and Deep Learning by deeplearning.ai on Coursera. Neural Networks and Deep Learning. 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. Neural Networks and Deep Learning. Share. Help . So, your mileage may vary. The prefilled assignment files are already completed. If nothing happens, download the GitHub extension for Visual Studio and try again. • Deep Learning View My GitHub Profile spmielke@gmail.com Deep Learning Specialization. (2016). Instructor: Andrew Ng, DeepLearning.ai. Sign in. Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance. Published: April 01, 2019. Be able to implement a neural network in TensorFlow. The repository contains files for Course 1, 2, 3. Contribute to DoDuy/Deep-Learning-Specialization development by creating an account on GitHub. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content. This repo contains all my work for this specialization. Insert . deeplearning.ai. 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. If nothing happens, download Xcode and try again. Deep Learning Specialization on Coursera. Click to connect. Can’t wait to apply some of the idea in my research work. If nothing happens, download GitHub Desktop and try again. Open settings. deepanshut041.github.io/deep-learning-specialization/, download the GitHub extension for Visual Studio, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. Introduction to 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. This is my personal projects for the course. This is the repository for my implementations on the Deep Learning Specialization from Coursera. 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 Deep Learning Specialization on Coursera. less than 1 minute read. Know to use neural style transfer to generate art. Done and pass 100% all Quiz and Programming Assignments. Deep Learning Specialization courses by Andrew Ng, deeplearning.ai - AdalbertoCq/Deep-Learning-Specialization-Coursera All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera. One of the Deep Learning Specialization job in “ Deep Learning by deeplearning.ai on Coursera created this post. Which we will teach taught by two experts in NLP, machine Learning, and into. 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