November 28, 2020

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Four Courses Specialization Tensorflow in practise Specialization

repo name ashishpatel26/Tensorflow-in-practise-Specialization
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created 2019-08-13

My Certification :

Course 1: Introduction to TensorFlow for AI, ML and DL

This first course introduces you to Tensor Flow, a popular machine learning framework. You will learn how to build a basic neural network for computer vision and use convolutions to improve your neural network.

Week 1: A New Programming Paradigm

  • Introduction: A conversation with Andrew Ng
  • A primer in machine learning
  • The “Hello World” of neural networks
  • Working through “Hello World” in TensorFlow and Python
  • Week 1 - Predicting house price.ipynb

Week 2: Introduction to Computer Vision

  • A conversation with Andrew Ng
  • An introduction to computer vision
  • Writing code to load training data
  • Coding a computer vision neural network
  • Walk through a notebook for computer vision
  • Using callbacks to control training
  • Walk through a notebook with callbacks
  • Week 2 - Classifying Fashion MNIST with MLP.ipynb

Week 3: Enhancing Vision with Convolutional Neural Networks

  • A conversation with Andrew Ng
  • What are convolutions and pooling?
  • Implementing convolutional layers
  • Implementing pooling layers
  • Improving the fashion classifier with convolutions
  • Walking through convolutions
  • Week 3 - Classifying Fashion MNIST with CNN.ipynb

Week 4: Using Real-World Images

  • A conversation with Andrew Ng
  • Understanding ImageGenerator
  • Defining a ConvNet to use complex images
  • Training the ConvNet with fit_generator
  • Walking through developing a ConvNet
  • Walking through training the ConvNet with fit_generator
  • Adding automatic validation to test accuracy
  • Exploring the impact of compressing images
  • Outro: Conversation with Andrew
  • Week 4 - Classifying emotion with CNN.ipynb

Course 2: Convolutional Neural Networks in TensorFlow

This second course teaches you advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropouts. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models.

Week 1: Exploring a Larger Dataset

  • Introduction: A conversation with Andrew Ng
  • Training with the cats vs. dogs dataset
  • Working through the notebook
  • Fixing through cropping
  • Looking at accuracy and loss
  • Week 1 - Classifying Cats and Dogs.ipynb

Week 2: Augmentation, a Technique to Avoid Overfitting

  • A conversation with Andrew Ng
  • Introducing augmentation
  • Coding augmentation with ImageDataGenerator
  • Demonstrating overfitting in cats vs. dogs dataset
  • Adding augmentation to cats vs. dogs dataset
  • Exploring augmentation with horses vs. humans dataset
  • Week 2 - Improving Cats and Dogs Classifier.ipynb

Week 3: Transfer Learning

  • A conversation with Andrew Ng
  • Understanding transfer learning: the concepts
  • Coding your own model with transferred features
  • Exploring dropouts
  • Exploring transfer learning with inception
  • Week 3 - Transfer learning (VGG Net).ipynb

Week 4: Multi-class Classifications

  • A conversation with Andrew Ng
  • Moving from binary to multi-class classification
  • Exploring multi-class classification with the rock paper scissors dataset
  • Training a classifier with the rock paper scissors dataset
  • Testing the rock paper scissors classifier
  • Week 4 - Classifying images of sign languages.ipynb

Course 3: Natural Language Processing in TensorFlow

In this third course, you’ll learn how to apply neural networks to solve natural language processing problems using TensorFlow. You’ll learn how to process and represent text through tokenization so that it’s recognizable by a neural network. You’ll be introduced to new types of neural networks, including RNNs, GRUs and LSTMs, and how you can train them to understand the meaning of text. Finally, you’ll learn how to train LSTMs on existing text to create original poetry and more!

Week 1: Sentiment in Text

Week 2: Word Embeddings

Week 3: Sequence Models

Week 4: Sequence Models and Literature

Course 4: Sequences, Time Series, and Prediction

In this fourth course, you will learn how to solve time series and forecasting problems in TensorFlow. You’ll first implement best practices to prepare data for time series learning. You’ll also explore how RNNs and ConvNets can be used for predictions. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data!

Week 1: Sequences and Prediction

Week 2: Deep Neural Networks for Time Series

Week 3: Recurrent Neural Networks for Time Series

Week 4: Real-world Time Series Data

  • A conversation with Andrew Ng
  • Convolutions
  • Bi-directional LSTMs
  • Real data – sunspots
  • Train and tune the model
  • Prediction
  • Sunspots
  • Combining our tools for analysis
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