Результаты поиска по запросу: [Udemy] Unsupervised Deep Learning in Python (Lazy Programmer Inc.)

Autoencoders + Restricted Boltzmann Machines for Deep Neural Networks in Theano, + t-SNE and PCA This course is the next logical step in my deep learning, data science, and machine learning series. I’ve done a lot of courses about deep learning, and I just released a course about unsupervised...

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE. Cluster analysis is a staple of unsupervised machine learning and data science. It is very useful for data mining and big data because it automatically finds patterns in the...

HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank. The Hidden Markov Model or HMM is all about learning sequences. A lot of the data that would be very useful for us to model is in sequences. Stock prices are sequences of prices. Language is a sequence of...

A guide for writing your own neural network in Python and Numpy, and how to do it in Google's TensorFlow. This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic...

Take deep learning to the next level with SGD, Nesterov momentum, RMSprop, Theano, TensorFlow, and using the GPU on AWS. This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a...

Data science techniques for professionals and students - learn the theory behind logistic regression and code in Python This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic...

Data science: Learn linear regression from scratch and build your own working program in Python for data analysis. This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of...

GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences *** NOW IN TENSORFLOW 2 and PYTHON 3 *** Learn about one of the most powerful Deep Learning architectures yet! The Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence...

Computer Vision and Data Science and Machine Learning combined! In Theano and TensorFlow *** NOW IN TENSORFLOW 2 and PYTHON 3 *** Learn about one of the most powerful Deep Learning architectures yet! The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in...

Complete guide on deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets In this course we are going to look at NLP (natural language processing) with deep learning. Previously, you learned about some of the basics, like how many NLP problems are...

A-Z Guide to Implementing Classic Machine Learning Algorithms From Scratch and with Sci-Kit Learn In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and...

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and more This course is all about A/B testing. A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more. A/B testing is all about comparing things. If...

Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images...

Advanced AI: Deep Reinforcement Learning in Python Полное руководство по освоению искусственного интеллекта с помощью глубокого обучения и нейронных сетей. Чему вы научитесь: Создание различных агентов глубокого обучения (включая DQN и A3C) Применяйте различные передовые алгоритмы обучения с...

A-Z guide to practical NLP: spam detection, sentiment analysis, article spinners, and latent semantic analysis. In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech...

Machine Learning and AI: Support Vector Machines in Python Искусственный интеллект и наука о данных Алгоритмы классификации и регрессии на Python Чему вы научитесь: Применяйте SVM в практических приложениях: распознавание изображений, обнаружение спама, медицинская диагностика и регрессионный...

Здравствуйте, друзья! Добро пожаловать в раздел "Машинное обучение: Обработка естественного языка на Python (версия 2). Это обширный курс "4 в 1", включающий в себя: Векторные модели и методы предварительной обработки текста Вероятностные модели и марковские модели Методы машинного обучения...

Язык: Английский Полное руководство по получению и реализации word2vec, GloVe, вкраплений слов и анализа настроения с помощью рекурсивных сетей Чему вы научитесь: Понять и реализовать word2vec Понять метод CBOW в word2vec Понять метод пропусков в word2vec Понять оптимизацию отрицательной...

Tensorflow 2: Deep Learning & Artificial Intelligence Машинное обучение и нейронные сети для компьютерного зрения, анализа временных рядов, обработки естественного языка, генеративно-состязательных сетей, обучения с подкреплением и многого другого! Чему Вы научитесь: Искусственные нейронные...

Нейронные сети для компьютерного зрения, прогнозирования временных рядов, НЛП, GANs, Reinforcement Learning и многое другое! Требования: Уметь писать код на Python и Numpy Для теоретической части (по выбору) - понимание производных и вероятности Описание: Вы когда-нибудь задумывались...