Deep Learning with TensorFlow

Uplatz provides this in-depth course on Deep Learning with TensorFlow. It is a self-paced course consisting of video tutorials. You will be awarded Course Completion Certificate at the end of the course.

TensorFlow is a framework created by Google for creating Deep Learning models. Deep Learning is a category of machine learning models (=algorithms) that use multi-layer neural networks. Whether it has to do with images, videos, text or even audio, Machine Learning can solve problems from a wide range.

Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. TensorFlow bundles together a slew of machine learning and deep learning (aka neural networking) models and algorithms and makes them useful by way of a common metaphor.

TensorFlow is an open source library for fast numerical computing. ... Unlike other numerical libraries intended for use in Deep Learning like Theano, TensorFlow was designed for use both in research and development and in production systems, not least RankBrain in Google search and the fun DeepDream project.

In this online course by Uplatz, you'll learn how to build deep learning applications with TensorFlow. You'll explore the tools and software developers use to build scalable AI-powered algorithms in TensorFlow.

Certificate of completion

Digital certificate - Included

Reed courses certificate of completion

Digital certificate - Included

• Deep Learning with TensorFlow - all lectures 29:19:24

  • 1: TensorFlow Introduction Preview 19:20
  • 2: TensorFlow Applications 15:19
  • 3: TensorFlow Basics - part 1 17:13
  • 4: TensorFlow Basics - part 2 14:27
  • 5: TensorFlow Components - part 1 19:33
  • 6: TensorFlow Components - part 2 17:33
  • 7: TensorFlow Pipeline 10:10
  • 8: TensorFlow Examples 19:40
  • 9: Introduction to Linear Algebra - part 1 33:07
  • 10: Introduction to Linear Algebra - part 2 27:09
  • 11: Introduction to Python - part 1 13:55
  • 12: Introduction to Python - part 2 13:39
  • 13: Introduction to Python - part 3 11:21
  • 14: Introduction to Python - part 4 06:46
  • 15: Introduction to Python - part 5 08:40
  • 16: Introduction to Python - part 6 16:32
  • 17: Introduction to Python - part 7 22:13
  • 18: Introduction to Python - part 8 18:40
  • 19: Introduction to Python - part 9 16:53
  • 20: Introduction to Python - part 10 18:40
  • 21: Introduction to Python - part 11 10:36
  • 22: Introduction to Python - part 12 06:37
  • 23: Introduction to Python - part 13 32:18
  • 24: Introduction to Python - part 14 20:49
  • 25: Introduction to Python - part 15 17:06
  • 26: Introduction to Matplotlib 1:04:10
  • 27: Introduction to NumPy - part 1 24:43
  • 28: Introduction to NumPy - part 2 29:55
  • 29: Introduction to Pandas - part 1 15:57
  • 30: Introduction to Pandas - part 2 05:54
  • 31: Introduction to Pandas - part 3 32:00
  • 32: Introduction to Pandas - part 4 20:59
  • 33: Introduction to Pandas - part 5 28:10
  • 34: Introduction to Pandas - part 6 15:19
  • 35: Introduction to Pandas - part 7 08:09
  • 36: Introduction to Pandas - part 8 18:57
  • 37: File Management - part 1 14:09
  • 38: File Management - part 2 23:38
  • 39: File Management - part 3 14:49
  • 40: Machine Learning - part 1 27:27
  • 41: Machine Learning - part 2 17:31
  • 42: Machine Learning - part 3 17:36
  • 43: Machine Learning - part 4 15:39
  • 44: Machine Learning - part 5 13:54
  • 45: Machine Learning - part 6 11:56
  • 46: Machine Learning - part 7 18:52
  • 47: Machine Learning - part 8 23:56
  • 48: Machine Learning - part 9 22:38
  • 49: Machine Learning - part 10 29:13
  • 50: Machine Learning - part 11 08:09
  • 51: Machine Learning - part 12 35:12
  • 52: Machine Learning - part 13 15:03
  • 53: Machine Learning - part 14 16:18
  • 54: Machine Learning - part 15 13:58
  • 55: Machine Learning - part 16 19:45
  • 56: Machine Learning - part 17 05:13
  • 57: Machine Learning - part 18 31:39
  • 58: Machine Learning - part 19 28:20
  • 59: Machine Learning - part 20 17:36
  • 60: Machine Learning - part 21 25:09
  • 61: Machine Learning - part 22 31:56
  • 62: TensorFlow Playground 36:00
  • 63: TensorFlow Perceptrons - part 1 22:00
  • 64: TensorFlow Perceptrons - part 2 16:45
  • 65: TensorFlow Perceptrons - part 3 12:00
  • 66: TensorFlow and Artificial Intelligence 19:40
  • 67: TensorFlow ANN 11:20
  • 68: Types of ANN - part 1 21:55
  • 69: Types of ANN - part 2 17:10
  • 70: Components of Neural Networks 14:25
  • 71: Classification in TensorFlow - part 1 14:08
  • 72: Classification in TensorFlow - part 2 17:00
  • 73: Classification in TensorFlow - part 3 20:00
  • 74: Classification in TensorFlow - part 4 25:00
  • 75: Classification in TensorFlow - part 5 30:25
  • 76: Linear Regression in TensorFlow 37:10
  • 77: Difference between TensorFlow, PyTorch, Theano, Keras 21:30
  • 78: Object Identification in TensorFlow 08:10
  • 79: Super Keyword - part 1 21:00
  • 80: Super Keyword - part 2 32:40
  • 81: Super Keyword - part 3 18:00
  • 82: CNN - part 1 16:00
  • 83: CNN - part 2 22:10
  • 84: CNN - part 3 24:00
  • 85: RNN - part 1 13:35
  • 86: RNN - part 2 17:04
  • 87: RNN Time Series - part 1 29:42
  • 88: RNN Time Series - part 2 17:29
  • 89: TensorBoard 11:01

Deep Learning with TensorFlow - course syllabus

Module 1 – Introduction to TensorFlow

• HelloWorld with TensorFlow
• Linear Regression
• Nonlinear Regression
• Logistic Regression
• Activation Functions

Module 2 – Convolutional Neural Networks (CNN)

• CNN History
• Understanding CNNs
• CNN Application

Module 3 – Recurrent Neural Networks (RNN)

• Intro to RNN Model
• Long Short-Term memory (LSTM)
• Recursive Neural Tensor Network Theory
• Recurrent Neural Network Model

Module 4 - Unsupervised Learning

• Applications of Unsupervised Learning
• Restricted Boltzmann Machine
• Collaborative Filtering with RBM

Module 5 - Autoencoders

• Introduction to Autoencoders and Applications
• Autoencoders
• Deep Belief Network

Everyone

Passion to learn and succeed!

• Data Scientist - Deep Learning/TensorFlow
• Machine Learning Engineer
• Deep Learning Engineer
• AI Researcher & Developer - TensorFlow
• Python Machine Learning Engineer
• Lead Engineer/Architect
• AI Developer
• Business Analyst
• Data Consultant
• Data Strategist/Architect/Engineer
• Machine Learning Framework Developer - Python/TensorFlow
• TensorFlow Inference Engineer
• NLP Engineer
• Senior Computer Vision Engineer

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