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5 Students
93 Lessons
Beginner

What Will You Learn?

  • Overview of Tensorflow 2.0, PyTorch, MXNet and OpenCV modules, APIs and installation.
  • Build Convolutional Neural Network CNN models using Tensorflow 2.0, PyTorch and MXNet
  • Build Recurrent Neural Network RNN models using Tensorflow 2.0, PyTorch and MXNet
  • Build Fully Connected Network FCN models using Tensorflow 2.0, PyTorch and MXNet
  • Implement Transfer Learning using using Tensorflow 2.0, PyTorch and MXNet

Curriculum

3h 30m
Section 2: TensorFlow 2.0
51:15
TensorFlow course objective
01:33
TensorFlow course methodology
01:55
TensorFlow modules and API
01:07
TensoFlow changes and concepts
02:36
TensorFlow data pipeline
01:50
TensorFlow tf data code walk-through
03:24
TensorFlow data augmentation
01:16
TensorFlow Keras walk-through
02:06
TensorFlow fully connected nn model
01:45
TensorFlow fully connected model with data pipeline code walk-through
06:05
TensorFlow CNN model steps walk-through
01:53
TensorFlow CNN model code walk-through
04:21
TensorFlow RNN-based sequence models
01:48
TensorFlow RNN code walk-through
01:25
TensorFlow transfer learning walk-through
02:13
TensorFlow entire workflow with transfer learning code advance walk-through
06:57
TensorFlow Quiz
02:00
TensorFlow exercise tasks
01:13
TensorFlow exercise solution walk-through
01:53
TensorFlow exercise 2
00:43
TensorFlow exercise 2 solution walk-through
01:52
TensorFlow course summary
01:20
Section 3: MXnet
41:35
MXNet introduction and course benefit
01:37
MXnet course coverage methodology
01:15
MXNet modules and APIs
02:32
MXNet NDArray
00:57
MXNet data augumentation and tranformation
01:49
MXNet data pipeline transformation code walk-through
02:01
MXNet deep learning model building steps
02:01
MXNet deep learning FCN code walk-through
02:53
MXNet CNN model building steps
01:54
MXNet deep learning CNN model code walk-through
04:38
MXNet RNN model steps
01:44
MXNet RNN code walk-through
05:29
MXNet transfer learning steps
01:38
MXnet transfer learning code advance walk-through
03:42
MXnet Quiz
MXNet exercise
00:55
MXNet exercise solution code walk-through
03:30
MXNet exercise 2 overview
00:46
MXNet exercise 2 solution walk-through
01:03
MXNet course summary
01:11
Section 4: PyTorch
50:40
PyTorch course introduction
01:55
PyTorch course coverage methology
01:17
PyTorch installation procedure
01:54
PyTorch modules and concepts
03:26
PyTorch Torch API code walk-through
02:33
PyTorch data pipeline
01:10
PyTorch data pipeline and tranformation code walk-through
01:29
PyTorch data transformation
01:22
PyTorch torchnn for configuring the deep learning models
01:56
PyTorch FCN code walk-through
05:58
PyTorch steps for CNN model
01:40
PyTorch CNN code walk-through
04:38
PyTorch_RNN model construction walk-through
01:45
PyTorch RNN code walk-through
05:03
PyTorch_transfer learning using TorchVision
01:48
PyTorch transfe learning code advance walk-through
05:10
PyTorch Quiz
PyTorch exercise
01:12
PyTorch exercise solution walk-through
02:05
PyTorch exercise 2 overview
00:53
PyTorch exercise 2 solution walk-through
01:57
PyTorch course summary
01:29
Section 5: OpenCV
31:35
OpenCV introduction and course benefits 1
01:29
OpenCV course coverage methodology 1
01:17
OpenCV accessing image properties 1
01:13
OpenCV reading image and coverting back
01:19
OpenCV basic operations code walk-through
05:25
OpenCV image prcocessing 1
01:10
OpenCV image transformation code walk-through
07:27
OpenCV feature detection
01:04
OpenCV feature detection code advance walk-through
04:50
OpenCV Quiz
OpenCV exercise
00:59
OpenCV exercise solution
01:34
OpenCV exercise 2 overview
00:32
OpenCV exercise 2 solution walk-through
02:08
OpenCV course summary
01:08
Section 6: Big Secrets
06:08
Big secret MXNet Numpy interface
02:23
Big secret using TensorFlow graphics
01:42
Big secret using tfds dataset
02:03
Section 7: Capstone Project
07:06
Capstone project deep learning crash course
01:47
Capstone project solution walk-through
05:19
Section 8: Value Add-ons
06:02
Frequently asked questions
03:04
Additional resources for learning
02:58
Section 9: ALL Downloads
Jupyter notebooks and Course Files

Description

Requirements

  • Basic Python Programming

About the Instructor

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We, at Python Profits, have a goal to help people like you become more prepared for future opportunities in Data Science using Python.

The amount of data collected by businesses exploded in the past 20 years. But, the human skills to study and decode them have not caught up with that speed.

It is our goal to make sure that we are not left behind in terms of analyzing these pieces of information for our future.

This is why throughout the years, we’ve studied methods and hired experts in Data Science to create training courses that will help those who seek the power to become better in this field.

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