TensorFlow 2.0 Tutorials

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About Course

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 You 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

Course Content

TensorFlow 2.0 Tutorials

  • Anaconda Installation on Windows 10 | Python Installation on Windows 10 | Jupyter Notebook Install
    00:00
  • Anaconda Installation on Ubuntu 18.04 and 20.04
    00:00
  • Jupyter Notebook Keyboard Shortcuts
    00:00
  • Getting Started with Coding of Tensorflow 2.0 and Keras
    00:00
  • Building Your First ANN with TensorFlow 2.0 and Keras
    00:00
  • Plotting Learning Curve and Confusion Matrix in TensorFlow
    00:00
  • Plot Learning Curve and Confusion Matrix in TensorFlow 2.0
    00:00
  • 2D CNN in TensorFlow 2.0 for cifar10 Dataset Classificatio
    00:00
  • How to Download ML Dataset in Google Colab from Kaggle
    00:00
  • Use of Dropout and Batch Normalization in 2D CNN
    00:00
  • Object Classification Using TensorFlow and VGG16 Model
    00:00
  • Build an Accurate 2D CNN for MNIST Digit Recognition
    00:00
  • Breast Cancer Detection Using CNN in Python
    00:00
  • Bank Customer Satisfaction Prediction Using CNN
    00:00
  • Credit Card Fraud Detection using CNN in TensorFlow 2.0
    00:00
  • Multi-Label Image Classification on Movies Poster in CNN
    00:00
  • Human Activity Recognition using Accelerometer and CNN
    00:00
  • Malaria Parasite Detection Using CNN
    00:00
  • Google Stock Price Prediction Using RNN – LSTM
    00:00
  • IMDB Review Classification using RNN – LSTM
    00:00
  • Airlines Passenger Prediction using RNN – LSTM
    00:00
  • Multi Step Prediction using LSTM | Time Series Prediction
    00:00
  • MobileNet – Depthwise and Pointwise CNN Review
    00:00
  • NLP Tutorial 14 – TF2.0 and Keras for Word Embedding in NLP on Twitter Sentiment Data
    00:00

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