Deep Learning by TensorFlow 2.0 Basic to Advance with Python_1

Deep Learning by TensorFlow 2.0 Basic to Advance with Python, available at $19.99, has an average rating of 3.7, with 120 lectures, based on 26 reviews, and has 320 subscribers.

You will learn about 1. The content (80% hands on and 20% theory) will prepare you to work independently on Deep Learning projects 2. Foundation of Deep Learning TensorFlow 2.x 3. Use TensorFlow 2.x for Regression (2 models) 4. Use TensorFlow 2.x for Classifications (2 models) 5. Use Convolutional Neural Net (CNN) for Image Classifications (5 models) 6. CNN with Image Data Generator 7. Use Recurrent Neural Networks (RNN) for Sequence data (3 models) 8. Transfer learning 9. Generative Adversarial Networks (GANs) 10. Hyper parameters Tuning 11. How to avoid Overfitting 12. Best practices for Deep Learning and Award winning Architectures This course is ideal for individuals who are Want to Learn and Apply – Deep Learning by TensorFlow 2.x Python It is particularly useful for Want to Learn and Apply – Deep Learning by TensorFlow 2.x Python.

Enroll now: Deep Learning by TensorFlow 2.0 Basic to Advance with Python

Summary

Title: Deep Learning by TensorFlow 2.0 Basic to Advance with Python

Price: $19.99

Average Rating: 3.7

Number of Lectures: 120

Number of Published Lectures: 120

Number of Curriculum Items: 120

Number of Published Curriculum Objects: 120

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

1. The content (80% hands on and 20% theory) will prepare you to work independently on Deep Learning projects

2. Foundation of Deep Learning TensorFlow 2.x

3. Use TensorFlow 2.x for Regression (2 models)

4. Use TensorFlow 2.x for Classifications (2 models)

5. Use Convolutional Neural Net (CNN) for Image Classifications (5 models)

6. CNN with Image Data Generator

7. Use Recurrent Neural Networks (RNN) for Sequence data (3 models)

8. Transfer learning

9. Generative Adversarial Networks (GANs)

10. Hyper parameters Tuning

11. How to avoid Overfitting

12. Best practices for Deep Learning and Award winning Architectures

Who Should Attend

Want to Learn and Apply – Deep Learning by TensorFlow 2.x Python

Target Audiences

Want to Learn and Apply – Deep Learning by TensorFlow 2.x Python

As a practitioner of Deep Learning, I am trying to bring many relevant topics under one umbrella in the following topics. Deep Learning has been most talked about for the last few years and the knowledge has been spread across multiple places.

1. The content (80% hands-on and 20% theory) will prepare you to work independently on Deep Learning projects

2. Foundation of Deep Learning TensorFlow 2.x

3. Use TensorFlow 2.x for Regression (2 models)

4. Use TensorFlow 2.x for Classifications (2 models)

5. Use Convolutional Neural Net (CNN) for Image Classifications (5 models)

6. CNN with Image Data Generator

7. Use Recurrent Neural Networks (RNN) for Sequence data (3 models)

8. Transfer learning

9. Generative Adversarial Networks (GANs)

10. Hyperparameters Tuning

11. How to avoid Overfitting

12. Best practices for Deep Learning and Award-winning Architectures

Course Curriculum

Chapter 1: Introduction of Deep Learning and TensorFlow 2.x

Lecture 1: TensorFlow 2.x Introduction, Prerequisite and Training Content

Lecture 2: Installations , Technology , Folder structure and 1.x vs 2.x

Lecture 3: Why Deep Learning is emerging

Lecture 4: Deep-Learning-Working-components

Chapter 2: TensorFlow 2.0 Basic

Lecture 1: TensorFlow Basics code

Lecture 2: Tensor segmentation code

Lecture 3: Regression with Premade Estimators

Lecture 4: Regression by using tf.keras model layers

Lecture 5: Classifications using Premade Estimators

Lecture 6: Multiclass classification using Tensorflow Multi level

Chapter 3: TensorFlow 2.0 Intermediate

Lecture 1: Binary classification on Kaggle data using TensorFlow Multi level

Lecture 2: Explore few more ways to better classification

Lecture 3: How-to-Teach-Machines

Lecture 4: CNN-Showcase-of-multiplications

Lecture 5: Important-terms-in-Deep-Learning

Lecture 6: The-MNIST-Data

Lecture 7: CNN for Image (MNIST) classification

Lecture 8: Classwork

Lecture 9: Image Data Generator also known as Data Augmentation

Lecture 10: Image Data Generator – Data generation

Lecture 11: CNN with Image Data Generator

Lecture 12: Emotion recognition with CNNs

Lecture 13: Recurrent-Neural-Networks-Overview

Lecture 14: The-Vanishing-and-Exploding-Gradient-Overview

Lecture 15: LSTM-and-GRU-Architecture

Lecture 16: Univariate Time Series using LSTM_train_test_mode

Lecture 17: Univariate Time Series using LSTM_train_mode

Lecture 18: Multivariate Time Series using LSTM

Lecture 19: How to know models are good enough Bias vs Variance

Chapter 4: TensorFlow 2.0 Advanced

Lecture 1: Transfer learning – Definition and Usages

Lecture 2: Basic model

Lecture 3: Customize the model to recognize the classes in our dataset

Lecture 4: Use inbuilt model to recognize the classes in our dataset

Lecture 5: Customize inbuilt model to recognize the classes in our dataset

Lecture 6: How-to-avoid-Overfitting

Lecture 7: How to avoid Overfitting – L2 and L1

Lecture 8: How to avoid Overfitting -Dropout – Batch Normalization – Early Stopping

Lecture 9: Generative Adversarial Networks (GANs)

Lecture 10: Generative Adversarial Networks (GANs) – code

Lecture 11: Hyper-parameters-tuning-for-Deep-Learning-Models

Lecture 12: Hyperparameter tuning by keras tuner

Chapter 5: Miscellaneous

Lecture 1: Best-Practices-for-DL

Lecture 2: Award-winning-Architectures

Lecture 3: References-and-Updates

Chapter 6: Introduction of Deep Learning and TensorFlow 1.x

Lecture 1: TensorFlow Introduction and Prerequisite

Lecture 2: TensorFlow Training Content

Lecture 3: Deep Learning is emerging field

Lecture 4: What is TensorFlow

Lecture 5: Deep Learning – Working components

Chapter 7: Foundation of Deep Learning (TensorFlow and Keras)

Lecture 1: TensorFlow Basics code

Lecture 2: TensorFlow Placeholder code

Lecture 3: TensorFlow rank and Simple Equations

Lecture 4: Reduction and important operations

Lecture 5: TensorFlow Session

Lecture 6: Tensor segmentation

Lecture 7: TensorFlow – Various operations

Lecture 8: Eager execution

Chapter 8: TensorFlow and Keras for Regression

Lecture 1: Regression and Classifications overview

Lecture 2: Regression with Premade Estimators – code

Lecture 3: Tensorboard

Lecture 4: Regression using tf.keras model layers – code

Lecture 5: Regression by Keras

Lecture 6: linear regression using Core TensorFlow – 2 Independents only – code

Lecture 7: Core Tensorflow for multi variable regression – code

Chapter 9: TensorFlow and Keras for Classifications

Lecture 1: Classifications using Premade Estimators – code

Lecture 2: Multiclass classification using Core Tensorflow – code

Lecture 3: Multiclass classification using Tensorflow Multi level – code

Lecture 4: Multiclass classification using Keras – code

Chapter 10: Convolutional Neural Net (CNN): Image Classifications

Lecture 1: How to Teach Machines

Lecture 2: CNN Showcase of multiplications

Lecture 3: Important terms in Deep Learning

Lecture 4: The MNIST Data

Lecture 5: The MNIST Data exploration – code

Lecture 6: Using core TF for MNIST Softmax after flattening the data – code

Lecture 7: TF tf.keras model layers for MNIST Softmax after flattening the data – 1 – code

Lecture 8: TF tf.keras model layers for MNIST Softmax after flattening the data – 1 – code

Lecture 9: Building a CNN for MNIST using TF Layers without flattening the data – 1- code

Lecture 10: Building a CNN for MNIST using TF Layers without flattening the data – 2- code

Lecture 11: Keras CNN – 1- code

Lecture 12: Keras CNN – 2- code

Lecture 13: Kaggle Emotion recognition with CNNs using Keras – 1- code

Lecture 14: Kaggle Emotion recognition with CNNs using Keras – 2- code

Lecture 15: Kaggle Emotion recognition with CNNs using Keras – 3- code

Chapter 11: Recurrent Neural Networks (RNN) for Sequence data

Lecture 1: Recurrent Neural Networks – Overview

Lecture 2: The Vanishing and Exploding Gradient – Overview

Lecture 3: LSTM and GRU Architecture

Lecture 4: LSTM using Keras with train-test mode – 1 – code

Lecture 5: LSTM using Keras with train-test mode – 2 – code.mp4

Lecture 6: LSTM using Keras with train-test mode – 3 – code.mp4

Instructors

Shiv Onkar Deepak Kumar
AI Researcher and Consultant, Chief Data Scientist

Rating Distribution

1 stars: 2 votes

2 stars: 4 votes

3 stars: 1 votes

4 stars: 12 votes

5 stars: 7 votes

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