Link: Deep Learning with TensorFlow 2.0  Data Science Deep Learning Machine-Learning Scientific Libraries ... Learn about the updates being made to TensorFlow in its 2.0 version. We'll give an ... 8,767 students enrolled Created by 365 Careers, 365 Careers Team Gain a Strong Understanding of TensorFlow - Google's Cutting-Edge Deep Learning Framework Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow Set Yourself Apart with Hands-on Deep and Machine Learning Experience Grasp the Mathematics Behind Deep Learning Algorithms Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding Some basic Python programming skills You'll need to install Anaconda. We will show you how to do it in one of the first lectures of the course. All software and data used in the course are free. Data scientists, machine learning engineers, and AI researchers all have their own skillsets.
But what is that one special thing they have in common? They are all masters of deep learning. We often hear about AI, or self-driving cars, or the'algorithmic magic' at Google, Facebook, and Amazon. But it is not magic - it is deep learning. And more specifically, it is usually deep neural networks – the one algorithm to rule them all.
Complete Guide to TensorFlow for Deep Learning with Python by Jose Portilla will help you learn how to use Google's Deep Learning Framework, TensorFlow with Python. This Deep Learning TensorFlow course is for Python developers who want to learn the latest Deep Learning techniques with TensorFlow. You will understand how Neural Networks work. Then you will build your own Neural Network from scratch with Python. This Deep Learning TensorFlow tutorial will teach you to use TensorFlow for Classification and Regression Tasks.
Strong Understanding of TensorFlow 2.0 from Vary Beginning Artificial Neural Networks (ANNs) in Tensorflow 2.0 Convolutional Neural Networks (CNNs) in Tensorflow 2.0 Deep Neural Networks (DNNs) in TensorFlow 2.0 Deep Learning Algorithms from Scratch in Python Using NumPy, Pandas, Matplotlib and TensorFlow 2.0 Activation Function, Cost Function, Gradient Descent and Backpropagation Over-fitting, Under-fitting, Training, Validation, Testing, and Initialization Google has recently released TensorFlow 2.0, it has so many features that simplify the model development, maintenance, processes and performanceNow it is very easy to build and deploy AI models in practice! Deep Learning is one of the fastest growing field of Artificial Intelligence, Deep Learning models can solve very hard and complex tasks... Strong Understanding of TensorFlow 2.0 from Vary Beginning Artificial Neural Networks (ANNs) in Tensorflow 2.0 Convolutional Neural Networks (CNNs) in Tensorflow 2.0 Deep Neural Networks (DNNs) in TensorFlow 2.0 Deep Learning Algorithms from Scratch in Python Using NumPy, Pandas, Matplotlib and TensorFlow 2.0 Activation Function, Cost Function, Gradient Descent and Backpropagation Over-fitting, Under-fitting, Training, Validation, Testing, and Initialization Strong Understanding of TensorFlow 2.0 from Vary Beginning
BESTSELLER 4.5 (26,962 ratings) 122,893 students enrolled Created by 365 Careers, 365 Careers Team What you'll learn The course provides the entire toolbox you need to become a data scientist Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow Impress interviewers by showing an understanding of the data science field Learn how to pre-process data Understand the mathematics behind Machine Learning (an absolute must which other courses don't teach!) Start coding in Python and learn how to use it for statistical analysis Perform linear and logistic regressions in Python Carry out cluster and factor analysis Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn Apply your skills to real-life business cases Use state-of-the-art Deep Learning frameworks such as Google's TensorFlowDevelop a business intuition while coding and solving tasks with big data Unfold the power of deep neural networks Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations Requirements No prior experience is required. We will start from the very basics You'll need to install Anaconda. We will show you how to do that step by step Microsoft Excel 2003, 2010, 2013, 2016, or 365 Each of these topics builds on the previous ones. And you risk getting lost along the way if you don't acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics.