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What you'll learn?
- A solid foundation on Tensorflow
Course Overview
This course lays a solid foundation to TensorFlow, a leading machine learning library from Google AI team. You'll see how TensorFlow can create a range of machine learning models, custom deep neural networks to transfer learning models built by big tech giants. You will learn how to use and reuse tensorflow effectively and apply on industry relevant problems.
Pre-requisites
- Knowledge of at least one programming language
- Basic math and statistics
Target Audience
- Anyone who wants to study and build neural networks and deep learning using Google Tensorflow
Curriculum 51 Lectures 03:48:44
-
Section 1 : Introducing Tensorflow
- Lecture 2 :
- Why TensorFlow?
- Lecture 3 :
- What is TensorFlow?
- Lecture 4 :
- TensorFlow as an Interface
- Lecture 5 :
- Tensorflow as an Environment
- Lecture 6 :
- Tensors
- Lecture 7 :
- Computation Graph
- Lecture 8 :
- Skills Checklist
- Lecture 9 :
- Modules Covered
- Lecture 10 :
- Installing TensorFlow
- Lecture 11 :
- TensorFlow training
- Lecture 12 :
- Prepare Data
- Lecture 13 :
- Tensor Types
- Lecture 14 :
- Loss & Optimization
- Lecture 15 :
- Running your first TensorFlow program
-
Section 2 : Building Neural Networks using TensorFlow
- Lecture 1 :
- Back to Tensors
- Lecture 2 :
- TensorFlow Data Types
- Lecture 3 :
- CPU vs GPU vs TPU
- Lecture 4 :
- TensorFlow methods
- Lecture 5 :
- Introduction to Neural Networks
- Lecture 6 :
- Neural Network Architecture
- Lecture 7 :
- Linear Regression example revisited
- Lecture 8 :
- The Neuron
- Lecture 9 :
- Neural Network Layers
- Lecture 10 :
- The MNIST Dataset
- Lecture 11 :
- Coding MNIST NN Demo
- Lecture 12 :
- Summary
-
Section 3 : Deep Learning using TensorFlow
- Lecture 1 :
- Deepening the network
- Lecture 2 :
- Images & Pixels
- Lecture 3 :
- How humans recognise images
- Lecture 4 :
- Convolutional Neural Networks
- Lecture 5 :
- ConvNet Architecture
- Lecture 6 :
- Overfitting and Regularization
- Lecture 7 :
- Max Pooling and RELU activations
- Lecture 8 :
- Dropout
- Lecture 9 :
- Strides and Zero Padding
- Lecture 10 :
- Coding Deep ConvNets demo
- Lecture 11 :
- Debugging Neural Networks
- Lecture 12 :
- Visualising NN using Tensorboard
- Lecture 13 :
- Tensorboard continued
- Lecture 14 :
- Summary
-
Section 4 : Transfer Learning using Keras & TFLearn
- Lecture 1 :
- Transfer Learning Introduction
- Lecture 2 :
- Google Inception Model
- Lecture 3 :
- Retraining Google Inception with our own data demo
- Lecture 4 :
- Predicting new images
- Lecture 5 :
- Transfer Learning Summary
- Lecture 6 :
- Extending TensorFlow
- Lecture 7 :
- Keras Demo
- Lecture 8 :
- TFLearn Demo
- Lecture 9 :
- Keras & TFLearn comparison
- Lecture 10 :
- Summary and Conclusion
Our learners work at
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Instructor
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At UNP our vision is to make learning fun, fulfilling and personalized. We are working towards democratizing data science and breaking down the entry barrier to analytics and data science world. We are committed to develop and publish top-notch data science learning materials. The materials are designed to make the students ready for the data science industry. All the contents developed at UNP are digital, either as e-books, video lectures, VR classrooms. Apart from distributing contents to individuals, we provide support for learning materials for corporate clients. The learning materials are developed only by experienced data science professionals and professors from tier 1 universities. Every material goes through strict review procedure before it gets published. Every material coming out from UNP is accompanied by code snippets, application to industrial projects and tips to prepare for a job interviews. Aligned with our vision, UNP scholarship program is set to provides learning opportunities for students with financial challenges.
