Learn Python, NumPy, Pandas, Scikit-learn, TensorFlow 2.0, Keras, Regression, Classification, SVM, Decision Trees, Random Forests, Ensemble Learning, Dimensionality Reduction, Unsupervised Learning, Convolutional and Recurrent Neural Networks, Natural Language Processing, Autoencoders, Generative Adversarial Networks, Reinforcement Learning and More
Learn Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Reinforcement Learning
Learn Python, NumPy, Pandas, Scikit-learn, HDFS, ZooKeeper, Hive, HBase, NoSQL, Oozie, Flume, Sqoop, Spark, Spark RDD, Spark Streaming, Kafka, SparkR, SparkSQL, MLlib, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow 2, Keras, Convolutional & Recurrent Neural Networks, Autoencoders, and Reinforcement Learning
Learn how to boost deep learning algorithms on various processor platforms and custom accelerators; and how to make them more secure and reliable.
Learn Python, NumPy, Pandas, scikit-learn, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow 2, Keras, Convolutional & Recurrent Neural Networks, Autoencoders, and Reinforcement Learning
Deep Learning. Understanding TensorFlow, Introduction to Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Training Neural Networks, TensorFlow Across Devices, Autoencoders, Reinforcement Learning. Enroll now!
2 years degree program approved by UGC. Program managed by REVA Academy for Corporate Excellence in collaboration with CloudxLab
Certificate Course on Developing Deep Learning Applications Offered By Intel Corporation
Welcome to this project on Image Classification with Pre-trained InceptionV3 Network. This project aims to impart the knowledge of how to access the pre-trained models(here we get pre-trained Inception model) from Keras of TensorFlow 2, and appreciate its powerful classification capacity by making the model predict the class of an input image.
Understanding the pre-trained models is very important because this forms the basis of transfer learning. one of the most appreciated techniques to perform the classification of a different task thus reducing the training time, the number of iterations, and resource consumption. Learning about the pre-trained models and …
Welcome to this project on NYSE Closing Price Prediction. In this project, you will use Pandas, Keras, and Python in order to build a predictive model and apply it to predict the closing prices.
Time-series modeling has a huge demand in today's numbers-filled world. It has a wide variety of applications in sales s forecasting, prediction of meteorological elements like rainfall, economic forecasting in the financial worlds, and many more.
In this exercise, we shall understand how to predict stock market closing prices for a firm using GRU, a state-of-art deep learning algorithm for sequential data. We shall focus …