Google TensorFlow Hands-On with Python: Latest Guide for Deep Learning

Google TensorFlow Hands on with Python (Latest)

Google TensorFlow : Learn, Implement Deep Learning & master one of the corn

Created by : Rated :4.1 55732 views

Last Updated: 2024-11-14 05:11:25 English Course Type: Self Learning

A solid foundation on Tensorflow

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.

Knowledge of at least one programming language
Basic math and statistics

Anyone who wants to study and build neural networks and deep learning using Google Tensorflow

Section 1 : Introducing Tensorflow 15 Lectures 00:57:02
Lecture 1 :
Introduction to TensorFlow Preview
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 12 Lectures 00:53:46
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 14 Lectures 01:11:14
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 10 Lectures 00:46:42
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

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