Learn to use TensorFlow 2.0 for Deep Learning Leverage the Keras API to quickly build models that run on Tensorflow 2 Perform Image Classification with Convolutional Neural Networks Use Deep Learning for medical imaging Forecast Time Series data with Recurrent Neural Networks Use Generative Adversarial Networks (GANs) to generate images Use deep learning for style transfer Generate text with RNNs and Natural Language Processing Serve Tensorflow Models through an API Use GPUs for accelerated deep learning Learn to use TensorFlow 2.0 for Deep Learning This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand. We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes.

Before starting this course you must at least have an intermediate level of python, basic understanding of convolutional neural networks, and basic knowledge of Tensorflow. By the end of this course you will learn how to train very accurate convolutional neural networks to predict test images for binary class. You know enough to where if you want to go off on your own and use your own methods how to do that. Also appropriate parameters to use as well as data augmentation methods. It is explained in this course how to train multiclass as well.

Have you ever looked up to a starry night sky, seen a shooting star and made a wish? Well, look again and look carefully. Are you sure it is a shooting star, or could it be something else? Can you tell for sure? Well, maybe if your wish comes true, then you can tell with certainty that it was a shooting star, no? This Fall semester at New College of Florida, 7 students in the Applied Data Science master's program joined the world-wide effort in analyzing data collected from cameras watching the night skies.

Online text to speech converter with realistic natural sounding voices. AI voiceover generator for your videos, free TTS solution for Voiceover

As a data science enthusiast, I have tried many different things to boost my knowledge and experience. And in my opinion, the best way to gain experience in data science (apart from working in the industry) is to do Kaggle competitions. I did face tons of frustration when starting them out hence why I am writing this story. After doing more than 2–3 Kaggle competitions I started noticing a lot of useful patterns that I wish I have noticed much earlier. If you are starting out your first competition, I highly recommend you pay attention to these things since it will save you a lot of time.

Machine Learning with Javascript - Master Machine Learning from scratch using Javascript and TensorflowJS with hands-on projects. Created by Stephen G

Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content. Each and every topic has been taught extensively in depth to cover all the possible areas to understand the concept in most possible easy way.

This is step by step course on how to create book classification using machine learning. It covers Numpy, Pandas, Matplotlib, Scikit learn and Django and at the end predictive model is deployed on Django. Most of things machine learning beginner do not know is how they can deploy a created model. How to put created model into application? Training model and getting 80%, 85% or 90% accuracy does not matter. As Artificial Intelligence Engineer you should be able to put created model into application.

Artificial Intelligence is a revolutionary technology that is used in every sector today. The main objective of artificial intelligence is to make computers more independent with reasoning. Considering the significance of AI and its accelerated adoption, there has been a growing demand for AI talents in companies. Reports state that the demand for Artificial Intelligence jobs has jumped over 75% over the last four years. AI courses are getting more and more popular with every passing day. So, how do you capitalize on this fantastic opportunity?

Learn how to use Python, NumPy, Pandas, Data Visualization, Machine Learning, ML Model with deployment & More.. - Free Course. Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!