This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes. This is the tutorial you've been looking for to become a modern JavaScript machine learning master in 2020. It doesn't just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume.

「お父さんAIスケッチ」で使われている、TensorFlowとPython3で画像生成（GAN：Generative Adversarial Network。敵対的生成ネットワーク）に挑戦。TensorFlowによる開発手順も確認できます。

Data Science & Deep Learning for Business 20 Case Studies - Use Python to solve problems in Retail, Marketing, Product Recommendation, Customer Clustering, NLP, Forecasting & more! Machine Learning from Linear Regressions (polynomial & multivariate), K-NNs, Logistic Regressions, SVMs, Decision Trees & Random Forests Unsupervised Machine Learning with K-Means, Mean-Shift, DBSCAN, EM with GMMs, PCA and t-SNE Build a Product Recommendation Tool using collaborative & item/content based Hypothesis Testing and A/B Testing - Understand t-tests and p values Natural Langauge Processing - Summarize Reviews, Sentiment Analysis on Airline Tweets & Spam Detection To use Google Colab's iPython notebooks for fast, relaible cloud based data science work Deploy your Machine Learning Models on the cloud using AWS Advanced Pandas techniques from Vectorizing to Parallel Processsng Statistical Theory, Probability Theory, Distributions, Exploratory Data Analysis Predicting Employee Churn, Insurance Premiums, Airbnb prices, credit card fraud and who to target for donations Big Data skills using PySpark for Data Manipulation and Machine Learning Cluster customers based on Exploratory Data Analysis, then using K-Means to detect customer segments Build a Stock Trading Bot using re-inforement learning Apply Data Science & Analytics to Retail, performing segementation, analyzing trends, determining valuable customers and more! To use Google Colab's iPython notebooks for fast, relaible cloud based data science work 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!

Fuzzy Logic isn't often mentioned in the same room as Artificial Intelligence (AI). Pardon the pun, but most people find the idea of fuzzy logic to be fuzzy. However fuzzy logic has been working behind the scenes and bringing forth amazing technological advances for more than two decades. Fuzzy logic is a rule-based system that can rely on the practical experience of a data scientist or an expert. Fuzzy logic is a form of artificial intelligence, thus it is considered a subset of AI. Since it is performing a form of decision making, it can be included as a member of the AI family which includes Machine Learning and Deep Learning.

Dive into the concept of Artificial Intelligence and Machine Learning (ML) and learn how to implement advanced algorithms to solve real-world problems. This course will teach you the workflow of ML projects from data pre-processing to advanced model design and testing.

Learn how to code in Python Take your career to the next level Work with Python's conditional statements, functions, sequences, and loops Work with scientific packages, like NumPy Understand how to use the data analysis toolkit, Pandas Plot graphs with Matplotlib Use Python to solve real-world tasks Get a job as a data scientist with Python Acquire solid financial acumen Carry out in-depth investment analysis Build investment portfolios Calculate risk and return of individual securities Calculate risk and return of investment portfolios Apply best practices when working with financial data Use univariate and multivariate regression analysis Understand the Capital Asset Pricing Model Compare securities in terms of their Sharpe ratio Perform Monte Carlo simulations Learn how to price options by applying the Black Scholes formula Be comfortable applying for a developer job in a financial institution You'll need to install Anaconda. You'll need to install Anaconda. Do you want to learn how to use Python in a working environment? Are you a young professional interested in a career in Data Science? Would you like to explore how Python can be applied in the world of Finance and solve portfolio optimization problems?

The last several years have witnessed the great success of reinforcement learning (RL) including the video game playing [Mnih et al., 2015], robot manipulation [Gu et al., 2017], autonomous driving [Shalev-Shwartz et al., 2016] and many others [Lazic et al., 2018, Dalal et al., 2016]. Most of them focus on the problem where the system of interest evolves continuously with time, e.g., a trajectory of a tennis ball. However, the conventional research in RL may omit a category of system that evolves continuously and may be interrupted by stochastic events abruptly (see the jumps in Figure 1). Such system exists ubiquitously in the social and information science and therefore necessitates the research of reinforcement learning in these domains to extend its applicability in the real-world problems [Farajtabar et al., 2017, Wang et al., 2018], in which the agent seeks an optimal intervention policy so as to improve the future course of events. Concrete examples may include: - Social media. Social media website allows users to create and share content. Retweet can form as users resharing and broadcasting others' tweet to their friends and followers. Such stochastic events would steer the behaviors of other tweet users [Rizoiu et al., 2017]. At the same time, the platform (agent) may want to seek a policy to effectively mitigate the fake news by optimizing the performance of real news propagation over the network Farajtabar et al. [2017].

Applications of neural networks have achieved great success in various fields. A major extension of the standard neural networks is to make them stochastic, namely, to make the output a random function of the input. In a broad sense, stochastic neural networks include neural networks trained with dropout (Srivastava et al., 2014; Gal & Ghahramani, 2016), Bayesian networks (Mackay, 1992), variational autoencoders (VAE) (Kingma & Welling, 2013), and generative adversarial networks (Goodfellow et al., 2014). There are many reasons why one wants to make a neural network stochastic. Two main reasons are (1) regularization and (2) distribution modeling.

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.