If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network.
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.
Welcome to the course on Data Science & Deep Learning for Business 20 Case Studies! This course teaches you how Data Science & Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies. Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade! "I'm only half way through this course, but i have to say WOW. It's so far, a lot better than my Business Analytics MSc I took at UCL. The content is explained better, it's broken down so simply. Some of the Statistical Theory and ML theory lessons are perhaps the best on the internet! "It is pretty different in format, from others.
This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level! My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate.
AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In this course, you will learn: - The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI realistically can--and cannot--do - How to spot opportunities to apply AI to problems in your own organization - What it feels like to build machine learning and data science projects - How to work with an AI team and build an AI strategy in your company - How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.
Machine Learning and Deep learning techniques, in particular, are changing the way computers see and interact with the World. From augmented and mixed-reality applications to just gathering data, these new techniques are revolutionizing a lot of industries. OpenCV is a cross-platform library using which we can develop real-time computer vision applications. It mainly focuses on image processing, video capture, and analysis including features like face detection and object detection. This course is designed to give you a hands-on learning experience by going from the basic concepts to the most current in-depth Deep Learning methods for Computer Vision in use today.
Fundamental stuff of Python and its library Numpy What is the AI, Machine Learning and Deep Learning History of Machine Learning, Data Analysis with Pandas Turing Machine and Turing Test The Logic of Machine Learning such as Machine Learning models and algorithms, Gathering data, Data pre-processing, Training and testing the model etc. What is Artificial Neural Network (ANN) Tensor Operations in Python Python instructors on Udemy specialize in everything from software development to data analysis, and are known for their effective Machine learning isn't just useful for predictive texting or smartphone voice recognition. Tensorflow, Python tensorflow Convolutional Neural Network Recurrent Neural Network and LTSM Python instructors on Udemy specialize in everything from software development to data analysis, and are known for their effective, friendly Machine Learning, Python machine learning a-z Deep Learning, python machine learning a-z Machine Learning with Python Deep Learning with Python Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer, I am here to hel What is data science? We have more data than ever before. But data alone cannot tell us much about the world around us. What does a data scientist do? Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems. What are the most popular coding languages for data science? Python is the most popular programming language for data science. It is a universal language How do I learn Python on my own?
Free Coupon Discount - TensorFlow 2.0 Practical Advanced, Master Tensorflow 2.0, Google's most powerful Machine Learning Library, with 5 advanced practical projects Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard Students also bought Recommender Systems and Deep Learning in Python Machine Learning and AI: Support Vector Machines in Python Natural Language Processing with Deep Learning in Python Artificial Intelligence: Reinforcement Learning in Python Data Science: Deep Learning in Python Preview this Udemy Course GET COUPON CODE Description Google has recently released TensorFlow 2.0 which is Google's most powerful open source platform to build and deploy AI models in practice. Tensorflow 2.0 release is a huge win for AI developers and enthusiast since it enabled the development of super advanced AI techniques in a much easier and faster way. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Advanced Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab. This course will cover advanced, state-of-the–art AI models implementation in TensorFlow 2.0 such as DeepDream, AutoEncoders, Generative Adversarial Networks (GANs), Transfer Learning using TensorFlow Hub, Long Short Term Memory (LSTM) Recurrent Neural Networks and many more. The applications of these advanced AI models are endless including new realistic human photographs generation, text translation, image de-noising, image compression, text-to-image translation, image segmentation, and image captioning.
The history of deep learning goes back as far as 1943, when Walter Pitts and Warren McCulloch created a computer model based on the neural networks of the human brain. Today, if we asked a language model like GPT-3 to write an article about the history of deep learning, it might begin with that sentence. Many changes led from Pitts and McCulloch's early neural network to what we now call "deep learning": the addition of backpropagation (Yann LeCun and others), and the creation of "deep" networks with many "hidden layers" (Geoff Hinton and others) are perhaps the most important. And while early neural networks couldn't be programmed effectively (if at all) on the computers of their day, deep learning has now become commonplace. What was once couldn't even be implemented on the largest supercomputers run comfortably on your laptop.