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Tensorflow 2 & Keras: Deep Learning & Artificial Intelligence

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Tensorflow 2 & Keras: Deep Learning & Artificial Intelligence (Size: 2.7 GB)
  0 102.4 B
  1. CNN Transfer Learning.mp4 80.7 MB
  1. CNN Transfer Learning.srt 18.6 KB
  1. Concept of Machine Learning.mp4 44.6 MB
  1. Concept of Machine Learning.srt 11.4 KB
  1. Describe Artificial Intelligence and Machine Learning and Deep Learning.mp4 20.8 MB
  1. Describe Artificial Intelligence and Machine Learning and Deep Learning.srt 4.9 KB
  1. Google Colab Introduction.mp4 14.9 MB
  1. Google Colab Introduction.srt 6 KB
  1. Introduction to Autoencoders.mp4 71.4 MB
  1. Introduction to Autoencoders.srt 18.7 KB
  1. Neural Style Transfer.mp4 32.9 MB
  1. Neural Style Transfer.srt 6.4 KB
  1. Python for Data Analysis- Numpy.mp4 56.4 MB
  1. Python for Data Analysis- Numpy.srt 18.3 KB
  1. RNN INTRODUCTION.mp4 55.8 MB
  1. RNN INTRODUCTION.srt 18.5 KB
  1. What is CNN.mp4 24.7 MB
  1. What is CNN.srt 8.3 KB
  1. What is Keras.mp4 27.5 MB
  1. What is Keras.srt 6.1 KB
  1 0 B
  1. What is GANs.mp4 44.3 MB
  1. What is GANs.srt 15.9 KB
  2 1 KB
  10. Matplotlib Histogram.mp4 15.7 MB
  10. Matplotlib Histogram.srt 3.8 KB
  10. Tensorflow Introduction.mp4 44.4 MB
  10. Tensorflow Introduction.srt 14.2 KB
  11. Eager Execution.mp4 18.9 MB
  11. Eager Execution.srt 6.1 KB
  11. Matplotlib Scatter Plot.mp4 18.9 MB
  11. Matplotlib Scatter Plot.srt 5.9 KB
  12. Matplotlib Area Plot.mp4 16.2 MB
  12. Matplotlib Area Plot.srt 4.3 KB
  13. Matplotlib Pie Chart.mp4 9.8 MB
  13. Matplotlib Pie Chart.srt 3.1 KB
  14. Matplotlib Subplots.mp4 12.7 MB
  14. Matplotlib Subplots.srt 4.9 KB
  2. Anaconda Installation.mp4 33.8 MB
  2. Anaconda Installation.srt 8.1 KB
  2. CIFAR 10.mp4 59.3 MB
  2. CIFAR 10.srt 14.9 KB
  2. DCGAN INTRODUCTION.mp4 32 MB
  2. DCGAN INTRODUCTION.srt 6.8 KB
  2. Data Augmentation.mp4 97.4 MB
  2. Data Augmentation.srt 21.4 KB
  2. Introduction to Neural Network.mp4 67.1 MB
  2. Introduction to Neural Network.srt 20.5 KB
  2. LSTM.mp4 38.7 MB
  2. LSTM.srt 13.2 KB
  2. Neural Style Transfer Implementation part 1.mp4 87.9 MB
  2. Neural Style Transfer Implementation part 1.srt 15.8 KB
  2. Pandas Series.mp4 49.4 MB
  2. Pandas Series.srt 16.1 KB
  2. What is Supervised Machine Learning and Linear Regression Algorithm.mp4 42 MB
  2. What is Supervised Machine Learning and Linear Regression Algorithm.srt 8.5 KB
  2. Working of CNN.mp4 51.2 MB
  2. Working of CNN.srt 17.5 KB
  2. implementation of autoencoder.mp4 65.7 MB
  2. implementation of autoencoder.srt 16 KB
  2.1 Autoencoder_Sukanya.ipynb 170.2 KB
  2.1 Neural_Style_Transfer.ipynb 25.8 MB
  3. DCGAN Project.mp4 86.7 MB
  3. DCGAN Project.srt 17.3 KB
  3. Fashion MNIST Part 1.mp4 66.6 MB
  3. Jupyter Notebook.mp4 14 MB
  3 1 KB
  3. Fashion MNIST Part 1.srt 15.9 KB
  3. Jupyter Notebook.srt 5.1 KB
  3. MNIST digit classification.mp4 69.1 MB
  3. MNIST digit classification.srt 17.2 KB
  3. Neural Style Transfer Implementation part 2.mp4 76.3 MB
  3. Neural Style Transfer Implementation part 2.srt 12 KB
  3. Pandas DataFrames.mp4 44.5 MB
  3. Pandas DataFrames.srt 11.5 KB
  3. Text classification.mp4 71.5 MB
  3. Text classification.srt 17.7 KB
  3. Types of Classification Problem.mp4 28.9 MB
  3. Types of Classification Problem.srt 10.2 KB
  3. What is UnSupervised Machine Learning.mp4 47.2 MB
  3. What is UnSupervised Machine Learning.srt 11 KB
  3.1 DCGAN.ipynb 214.4 KB
  3.1 Fashion_MNIST.ipynb 328.1 KB
  3.1 mnist_digits.ipynb 131.8 KB
  4. Activation Function part 1.mp4 46.9 MB
  4. Activation Function part 1.srt 19.8 KB
  4. Fashion MNIST Part 2.mp4 65.4 MB
  4. Fashion MNIST Part 2.srt 13.6 KB
  4. Grouping and Filtering.mp4 35.4 MB
  4. Grouping and Filtering.srt 9.3 KB
  4. cat dog classification.mp4 79.6 MB
  4. cat dog classification.srt 16.4 KB
  4. practical approach to word embedding.mp4 38.1 MB
  4. practical approach to word embedding.srt 12.2 KB
  4 1.3 KB
  4.1 Copy_of_CNN_DOG_CAT_Colab.ipynb 3.2 MB
  5. Activation Function part 2.mp4 37.1 MB
  5. Activation Function part 2.srt 15.2 KB
  5. Bidirectional neural network.mp4 20 MB
  5. Bidirectional neural network.srt 5.2 KB
  5. Fashion MNIST Part 3.srt 14.7 KB
  5. Slicing and Sorting.mp4 26.9 MB
  5. Slicing and Sorting.srt 7.7 KB
  5. cat dog classification 2.mp4 55.4 MB
  5 100.4 KB
  5. Fashion MNIST Part 3.mp4 61.6 MB
  5. cat dog classification 2.srt 11.4 KB
  6. Forward Propogation.mp4 24.9 MB
  6. Forward Propogation.srt 7.8 KB
  6 700 KB
  6. Pandas Missing Values.mp4 47.9 MB
  6. Pandas Missing Values.srt 11.7 KB
  6. facial expression recognition 1.mp4 83.6 MB
  6. facial expression recognition 1.srt 18 KB
  7. Back Propogation.mp4 33.5 MB
  7. Back Propogation.srt 8.4 KB
  7. Pandas Aggregation Functions.mp4 29.5 MB
  7. Pandas Aggregation Functions.srt 8 KB
  7. facial expression recognition 2.mp4 34.7 MB
  7 20.3 KB
  7. facial expression recognition 2.srt 5.8 KB
  7.1 Copy_of_Facial_Expression_Training.ipynb 535.7 KB
  8 478 KB
  8. Chain Rule.mp4 24.5 MB
  8. Chain Rule.srt 8 KB
  8. Matplotlib Introduction.mp4 45.5 MB
  8. Matplotlib Introduction.srt 13.7 KB
  8. leaf diseases 1.mp4 74 MB
  8. leaf diseases 1.srt 14.8 KB
  9. Gradient Descent.mp4 60.9 MB
  9. Gradient Descent.srt 12.5 KB
  9. Matplotlib Bar Graphs.mp4 24.9 MB
  9. Matplotlib Bar Graphs.srt 5.7 KB
  9. leaf diseases 2.mp4 40.7 MB
  9. leaf diseases 2.srt 9.1 KB
  TutsNode.com.txt 102.4 B
  [TGx]Downloaded from torrentgalaxy.to .txt 614.4 B
  9 571.4 KB
  10 935.9 KB
  11 927 KB
  12 441.5 KB
  13 298.9 KB
  14 592.7 KB
  15 375.4 KB
  16 108.6 KB
  17 670.4 KB
  18 623 KB
  19 195.1 KB
  20 598.3 KB
  21 816.7 KB
  22 567.9 KB
  23 124.1 KB
  24 850.8 KB
  25 102.8 KB
  26 490.6 KB
  27 391.2 KB
  28 480.6 KB
  29 573.2 KB
  30 718.1 KB
  32 324.2 KB
  33 279.4 KB
  34 884.3 KB
  35 959.1 KB
  36 649 KB
  37 264.1 KB
  38 204.3 KB
  39 559.6 KB
  40 82.4 KB
  41 614.4 B
  42 510.6 KB
  43 113.4 KB
  44 556.7 KB
  45 142.8 KB
  46 154.1 KB
  47 55.9 KB
  48 58.3 KB
  49 329.9 KB
  50 474.5 KB
  51 238.1 KB
  52 12.7 KB
  53 103.1 KB
  54 142.5 KB
  55 839.3 KB
  56 353.7 KB
  57 89.9 KB
  58 13.6 KB
  59 310.4 KB
  60 237.3 KB
  ▲ 189 total files

Description


Description

Welcome to Deep Learning and Artificial Intelligence with Tensorflow 2 and Keras API Course.

This course includes how to work with tensorflow 2 and creates Deep Learning applications with tensorflow 2 and Keras.

This course guide you how to work with google colab, all the hands on work done in google colab.

Many Projects included in this course like MNIST Digits Classification, MNIST Fashion data classification, Cat and Dog images Classification, Facial Expression Recognition, Leaf disease recognition, Generate Images with DCGANs(Deep Convolutional Generative Adversarial Networks) with Keras, Denoising autoencoders with Keras, TensorFlow, and Deep Learning etc.

Generative Deep Learning – Neural Style Transfer also included in this course.

For every lecture reference notes and code file is attached in this course.

Tensorflow is an open source machine library, and is one of the most widely used frameworks for deep learning.

Google released a new version of their TensorFlow deep learning library (TensorFlow 2) that integrated the Keras API directly and promoted this interface as the default or standard interface for deep learning development on the platform.

This course includes various topics –

Complete Understanding of TensorFlow 2.0 (Google’s Deep Learning Framework)from the Scratch
Keras API to quickly build models that run on Tensorflow 2
Learn How Neural Network works
Understand Backpropagation, Forward Propogation, Gradient Descent
Artificial Neural Networks (ANNs)
Convolutional Neural Networks (CNNs)
Perform Image Classification with Convolutional Neural Networks
Image Recognition
Recurrent Neural Networks (RNNs)
Transfer Learning
Create Generative Adversarial Networks (GANs) with TensorFlow
Autoencoders
Introduction to Natural Language Processing
Data Analysis with Numpy, Pandas and Data Visualization with Matplotlib

Who this course is for:

Anyone Passionate about Deep Learning and Artificial Intelligence
Python Developer curious about Deep Learning and Tensorflow

Requirements

Understanding of Python coding

Last Updated 5/2021

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