Search result for Lstm Online Courses & Certifications
FollowFilter ![]()
Filter ![]()
Showing 35 courses
By Category ![]()
By Subject![]()
By Language![]()
By Duration![]()
By Price![]()
Get Course Alerts by Email
AI Academy #3: Learn Artificial Neural Networks from A-Z
by Sobhan N.- 3.9
-
![]()
![]()
![]()
![]()
![]()
4.5 hours on-demand video
Next you are going to learn how to build RNN and LSTM network in python and keras environment....
$12.99
Natural Language Processing with Deep Learning in Python
by Lazy Programmer Team- 4.7
-
![]()
![]()
![]()
![]()
![]()
12 hours on-demand video
Can write a recurrent neural network / LSTM / GRU in Theano or TensorFlow from basic primitives, especially the scan function...
$39.99
Python & Machine Learning for Financial Analysis
by Dr. Ryan Ahmed, Ph.D., MBA- 4.5
-
![]()
![]()
![]()
![]()
![]()
23 hours on-demand video
Master Python Programming Fundamentals and Harness the Power of ML to Solve Real-World Practical Applications in Finance Are you ready to learn python programming fundamentals and directly apply them to solve real world applications in Finance and Banking? So why Python? 1. 2. 3. 4. 5. 6. This course is unique in many ways:...
$17.99
Complete 2-in-1 Python for Business and Finance Bootcamp
by Alexander Hagmann- 4.7
-
![]()
![]()
![]()
![]()
![]()
37.5 hours on-demand video
Data Science, Statistics, Hypothesis Tests, Regression, Simulations for Business & Finance: Python Coding AND Theory A-Z ######### UPDATE (August 2021) ############### Updated to latest Versions Added: Object-Oriented Programming (OOP) for complete Beginners: with real-world examples and in a way that everyone understands OOP! ########################################## Hi and welcome to this Course! This is the first-ever comprehensive Python Course for Business and Finance Professionals....
$16.99
Building Deep Learning Models with TensorFlow
by Samaya Madhavan , JEREMY NILMEIER , Romeo Kienzler , Alex Aklson- 4.4
-
![]()
![]()
![]()
![]()
![]()
Approx. 13 hours to complete
The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. Learning Outcomes: After completing this course, learners will be able to:...
The Complete Neural Networks Bootcamp: Theory, Applications
by Fawaz Sammani- 4.6
-
![]()
![]()
![]()
![]()
![]()
37.5 hours on-demand video
Master Deep Learning and Neural Networks Theory and Applications with Python and PyTorch! Including NLP and Transformers This course is a comprehensive guide to Deep Learning and Neural Networks. The theories are explained in depth and in a friendly manner. The course includes the following Sections: -------------------------------------------------------------------------------------------------------- Section 1 - How Neural Networks and Backpropagation Works...
$14.99
人工智慧：機器學習與理論基礎 (Artificial Intelligence - Learning & Theory)
by 于天立- 4.6
-
![]()
![]()
![]()
![]()
![]()
Approx. 12 hours to complete
本課程第二部分著重在和人工智慧密不可分的機器學習。課程內容包含了機器學習基礎理論（包含 1990 年代發展的VC理論）、分類器（包含決策樹及支援向量機）、神經網路（包含深度學習）及增強式學習（包含深度增強式學習。 此部份技術包含最早追溯至 1950 年代直到最近 2016 年附近的最新發展。此課程從基礎理論開始，簡介了各機器學習主流技法以及從淺層學習架構演變到最近深度架構的轉換。 本課程之核心目標為： （一）使同學對人工智慧相關的機器學習技術有基礎概念 （二）同學能夠理解機器學習基礎理論、分類器、神經網路、增強式學習 （三）同學能將相關技術應用到自己的問題上 修課前，基礎背景知識： 需要的先備知識：計算機概論 建議的先備知識：資料結構與演算法 Concept learning 1-1 Brief Introduction to Machine Learning, Learning from Example 1-2 Hypotheses ,Relation between Instance Space and Hypotheses 1-3 The Find-S Algorithm 1-4 Version Space and The List-Then Eliminate Algorithm 1-5 The Candidate Elimination Algorithm...
Related searches
Introduction to Machine Learning
by Lawrence Carin , David Carlson , Timothy Dunn , Kevin Liang- 4.7
-
![]()
![]()
![]()
![]()
![]()
Approx. 26 hours to complete
Use of LSTM for Text Synthesis...
Deep Learning and Reinforcement Learning
by Mark J Grover , Miguel Maldonado- 4.7
-
![]()
![]()
![]()
![]()
![]()
Approx. 14 hours to complete
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis....
Introduction to Deep Learning
by Evgeny Sokolov , Зимовнов Андрей Вадимович , Alexander Panin , Ekaterina Lobacheva , Nikita Kazeev- 4.5
-
![]()
![]()
![]()
![]()
![]()
Approx. 34 hours to complete
Modern RNNs: LSTM and GRU...
Previous
