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TensorFlow 2.0 Tutorials

  • Course level: Beginner

Description

What is Tensorflow 2.0?

Currently, the most famous #DeepLearning library in the world is Google’s TensorFlow. Google product uses machine learning in all of its products to improve the search engine, translation, image captioning, or recommendations.

Tensorflow architecture works in three parts:

Preprocessing the data

Build the model

Train and estimate the model

It is called Tensorflow because it takes input as a multi-dimensional array which is also known as tensors.

I am assuming that you know a little about machine learning and deep learning

Why Every Data Scientist Learn Tensorflow 2.x not Tensorflow 1.x

There are many changes in TensorFlow 2.0 to make users more productive, including removing redundant APIs, making APIs more consistent (Unified RNNs, Unified Optimizers), and better integrating with the Python runtime with Eager execution

API Cleanup

Eager execution

No more globals

Functions, not sessions (session.run())

Use Keras layers and models to manage variables

It is faster

It takes less space

More consistent

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What Will I Learn?

  • How to use Tensorflow 2.0 in Data Science
  • Important differences between Tensorflow 1.x and Tensorflow 2.0
  • How to implement Artificial Neural Networks in Tensorflow 2.0
  • How to implement Convolutional Neural Networks in Tensorflow 2.0
  • How to implement Recurrent Neural Networks in Tensorflow 2.0
  • How to build your own Transfer Learning application in Tensorflow 2.0
  • How to build a stock market trading bot using Reinforcement Learning (Deep-Q Network)
  • How to build Machine Learning Pipeline in Tensorflow 2.0

Topics for this course

24 Lessons

TensorFlow 2.0 Tutorials

Anaconda Installation on Windows 10 | Python Installation on Windows 10 | Jupyter Notebook Install00:00:00
Anaconda Installation on Ubuntu 18.04 and 20.0400:00:00
Jupyter Notebook Keyboard Shortcuts00:00:00
Getting Started with Coding of Tensorflow 2.0 and Keras00:00:00
Building Your First ANN with TensorFlow 2.0 and Keras00:00:00
Plotting Learning Curve and Confusion Matrix in TensorFlow00:00:00
Plot Learning Curve and Confusion Matrix in TensorFlow 2.000:00:00
2D CNN in TensorFlow 2.0 for cifar10 Dataset Classificatio00:00:00
How to Download ML Dataset in Google Colab from Kaggle00:00:00
Use of Dropout and Batch Normalization in 2D CNN00:00:00
Object Classification Using TensorFlow and VGG16 Model00:00:00
Build an Accurate 2D CNN for MNIST Digit Recognition00:00:00
Breast Cancer Detection Using CNN in Python00:00:00
Bank Customer Satisfaction Prediction Using CNN00:00:00
Credit Card Fraud Detection using CNN in TensorFlow 2.000:00:00
Multi-Label Image Classification on Movies Poster in CNN00:00:00
Human Activity Recognition using Accelerometer and CNN00:00:00
Malaria Parasite Detection Using CNN00:00:00
Google Stock Price Prediction Using RNN – LSTM00:00:00
IMDB Review Classification using RNN – LSTM00:00:00
Airlines Passenger Prediction using RNN – LSTM00:00:00
Multi Step Prediction using LSTM | Time Series Prediction00:00:00
MobileNet – Depthwise and Pointwise CNN Review00:00:00
NLP Tutorial 14 – TF2.0 and Keras for Word Embedding in NLP on Twitter Sentiment Data00:00:00
TensorFlow 2.0
69 £

Enrolment validity: Lifetime

Requirements

  • -Some maths basics like knowing what is a differentiation or a gradient
  • -Python basics