Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib. Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level. The easiest way to install deepface is to download it from PyPI. It's going to install the library itself and its prerequisites as well.

Later, in the 20th century, B.F. Skinner took both of these approaches and invented the operant conditioning chamber, or "Skinner Box". Unlike Edward Thorndike's puzzles, this box gave subjects (in this case mice), only one or two simple repeatable options. Using data from these experiments he and his collages defined operant conditioning as a learning process in which the strength of a behavior is modified by reinforcement or punishment. Why are we talking about all this? What does this mean to us, except that we need to have pets if we want to become a famous psychologist?

TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.

TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.

We will build a deep neural network that functions as part of an end-to-end machine translation pipeline. The completed pipeline will accept English text as input and return the French translation. For our model, we will use an English and French sample of sentences. The data is located in data/small_vocab_en and data/small_vocab_fr. The small_vocab_en file contains English sentences with their French translations in the small_vocab_fr file.

In this Machine Learning tutorial, we'll build a video game with Unity, TensorFlow and Python. We'll show you how easy it is to add ML-powered intelligence to video games or simulations, and how inference on smartphones is easier than it's ever been: modern, powerful tools like Unity's ML-Agents, Python, and TensorFlow make the complex easy. In this session, we'll build a little smartphone game, train a bot to play it using reinforcement learning, Python, and TensorFlow, and deploy it to a smartphone. We'll show you how easy it is to add ML-powered intelligence to video games or simulations, and how inference on smartphones is easier than it's ever been: modern, powerful tools like Unity's ML-Agents, Python, and TensorFlow make the complex easy. First, we'll spend 10 minutes of the session: Second, we'll spend 10 minutes of the session: Finally, we'll spend the last 10 minutes of the session: This is an engaging, fast-paced, and surprisingly in-depth exploration of how powerful modern game engines can be used for quick, relatively easy, but incredibly powerful state of the art machine learning and training, and how powerful inference on-device is, for mobile AI.

In this session, we'll build a little smartphone game, train a bot to play it using reinforcement learning, Python, and TensorFlow, and deploy it to a smartphone. We'll show you how easy it is to add ML-powered intelligence to video games or simulations, and how inference on smartphones is easier than it's ever been: modern, powerful tools like Unity's ML-Agents, Python, and TensorFlow make the complex easy. First, we'll spend 10 minutes of the session: * showcasing the absolute basics game engines * creating an arcade game, live on stage * adding some art, to make the game look pretty! Second, we'll spend 10 minutes of the session: * implementing an agent, using Python and TensorFlow, that is rewarded for playing the game * training the agent to play * giving the agent some character Finally, we'll spend the last 10 minutes of the session: * preparing our trained model for deployment onto a smartphone * building the game and optimizing both the gameplay and ML-components for a smartphone * showing the audience the game, running live on a phone! This is an engaging, fast-paced, and surprisingly in-depth exploration of how powerful modern game engines can be used for quick, relatively easy, but incredibly powerful state of the art machine learning and training, and how powerful inference on-device is, for mobile AI.

As a person coming from .NET world, it was quite hard to get into machine learning right away. One of the main reasons was the fact that I couldn't start Visual Studio and try out these new things in the technologies I am proficient with. I had to solve another obstacle and learn other programming languages more fitting for the job like Python and R. You can imagine my happiness when more than a year ago, Microsoft announced that as a part of .NET Core 3, a new feature will be available – ML.NET. In fact it made me so happy that this is the third time I write similar guide. Basically, I wrote one when ML.NET was a version 0.2 and one when it was version 0.10. Both times, guys from Microsoft decided to modify the API and make my articles obsolete. That is why I have to do it once again.

In this article, I will share my findings on creating a character-based Sequence-to-Sequence model (Seq2Seq) and I will share some of the results I have found. All of this is just a tiny part of my Master Thesis and it took quite a while for me to learn how to convert the theoretical concepts into practical models. I will also share the lessons that I have learned. This blog post is about Natural Language Processing (NLP in short). It is not easy for computers to interpret texts.