2023 Python for Deep Learning and Artificial Intelligence

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2023 Python for Deep Learning and Artificial Intelligence [TutsNode.net] - 2023 Python for Deep Learning and Artificial Intelligence 7. Introduction to Convolutional Neural Networks [Theory and Intuitions]
  • 14. MobileNet Architecture Explained.mp4 (121.3 MB)
  • 3. Convolutional Filters.mp4 (114.0 MB)
  • 15. EfficientNet Architecture Explained.mp4 (104.3 MB)
  • 5. Padding and Strides.mp4 (102.3 MB)
  • 11. AlexNet Architecture Explained.mp4 (98.7 MB)
  • 6. Pooling Layers.mp4 (86.5 MB)
  • 2. Working Principle of CNN.mp4 (80.2 MB)
  • 7. Activation Function.mp4 (72.7 MB)
  • 10. LeNet-5 Architecture Explained.mp4 (71.1 MB)
  • 12. GoogLeNet (Inception V1) Architecture Explained.mp4 (68.4 MB)
  • 4. Feature Maps.mp4 (66.9 MB)
  • 1. What is Convolutional Neural Network.mp4 (64.2 MB)
  • 13. RestNet Architecture Explained.mp4 (56.8 MB)
  • 9. CNN Architectures Comparison.mp4 (55.6 MB)
  • 8. Dropout.mp4 (32.4 MB)
5. End to End Deep Learning Project
  • 10. Data Visualization Part 2.mp4 (107.3 MB)
  • 5. What is Back Propagation.mp4 (79.4 MB)
  • 7. Steps to Build Neural Network.mp4 (64.1 MB)
  • 14. Neural Network Model Building.mp4 (60.9 MB)
  • 16. Model Training.mp4 (56.3 MB)
  • 2. Multi-Layer Perceptron.mp4 (55.1 MB)
  • 6. Optimizers in Deep Learning.mp4 (52.1 MB)
  • 19. Prediction on Real-Life Data.mp4 (50.9 MB)
  • 9. Data Visualization Part 1.mp4 (50.2 MB)
  • 15. Model Summary Explanation.mp4 (48.8 MB)
  • 4. Activation Function.mp4 (40.3 MB)
  • 12. Import Neural Networks APIs.mp4 (37.1 MB)
  • 11. Data Preprocessing.mp4 (36.4 MB)
  • 8. Customer Churn Dataset Loading.mp4 (26.0 MB)
  • 18. Model Save and Load.mp4 (23.6 MB)
  • 13. How to Get Input Shape and Class Weights.mp4 (21.2 MB)
  • 1. What is Neuron.mp4 (20.9 MB)
  • 17. Model Evaluation.mp4 (16.1 MB)
  • 3. Shallow vs Deep Neural Networks.mp4 (13.8 MB)
1. Course Setup
  • 1. Jupyter Notebook Introduction.mp4 (103.1 MB)
  • 1.1 python-for-deep-learning-and-ai.zip (74.7 MB)
10. Flowers Classification with Transfer Learning and CNN
  • 17. Online Prediction of Flowers Classes.mp4 (96.9 MB)
  • 2. Load Flowers Dataset for Classification.mp4 (68.0 MB)
  • 13. Make CNN Model with VGG16 Transfer Learning.mp4 (63.9 MB)
  • 7. How to Calculate Number of Parameters in CNN.mp4 (63.8 MB)
  • 15. Train Any Model for Transfer Learning.mp4 (63.3 MB)
  • 1. Transfer Learning Introduction.mp4 (57.4 MB)
  • 5. Preparing Data with Image Data Generator.mp4 (51.2 MB)
  • 11. import VGG16 from Keras.mp4 (51.0 MB)
  • 3. Download Flowers Data.mp4 (50.0 MB)
  • 4. Flowers Data Visualization.mp4 (48.7 MB)
  • 6. Baseline CNN Model Building.mp4 (46.4 MB)
  • 8. Baseline CNN Model Training.mp4 (46.3 MB)
  • 9. Train Model with TFDS Data Without Saving Locally Part 1.mp4 (41.3 MB)
  • 16. Save and Load Model with Class Names.mp4 (40.4 MB)
  • 10. Train Model with TFDS Data Without Saving Locally Part 2.mp4 (38.5 MB)
  • 12. Data Augmentation for Training.mp4 (25.6 MB)
  • 14. Model Training for Better Accuracy.mp4 (23.3 MB)
8. Horses vs Humans Classification with Simple CNN
  • 6. Data Display in Subplots Matrix.mp4 (89.2 MB)
  • 4. Download Humans or Horses Dataset Part 2.mp4 (76.0 MB)
  • 2. Introduction to TensorFlow Datasets (TFDS).mp4 (74.6 MB)
  • 5. Use of Image Data Generator.mp4 (73.4 MB)
  • 11. CNN Parameter Calculations Part 3.mp4 (61.4 MB)
  • 8. Building CNN Model.mp4 (60.7 MB)
  • 12. Model Training.mp4 (58.9 MB)
  • 3. Download Humans or Horses Dataset Part 1.mp4 (56.2 MB)
  • 7. CNN Introduction.mp4 (53.0 MB)
  • 14. Image Class Prediction.mp4 (52.3 MB)
  • 9. CNN Parameter Calculation.mp4 (44.8 MB)
  • 1. Overview of Image Classification using CNNs.mp4 (44.1 MB)
  • 10. CNN Parameter Calculations Part 2.mp4 (43.2 MB)
  • 13. Model Load and Save.mp4 (32.1 MB)
6. Introduction to Computer Vision with Deep Learning
  • 4. Fashion MNIST Dataset Analysis.mp4 (87.8 MB)
  • 8. Discovering Overfitting - Early Stopping.mp4 (77.5 MB)
  • 7. Model Summary and Training.mp4 (65.0 MB)
  • 3. Fashion MNIST Dataset Download.mp4 (63.2 MB)
  • 9. Model Save and Load for Prediction.mp4 (44.7 MB)
  • 1. Introduction to Computer Vision with Deep Learning.mp4 (43.0 MB)
  • 6. Deep Neural Network Model Building.mp4 (36.5 MB)
  • 2. 5 Steps of Computer Vision Model Building.mp4 (27.7 MB)
  • 5. Train Test Split for Data.mp4 (25.8 MB)
9. Building Cats and Dogs Classifier with Regularized CNN
  • 21. Load Model and Do the Prediction.mp4 (83.4 MB)
  • 12. Load Dataset for Baseline Classifier.mp4 (82.9 MB)
  • 8. Other Types of Data Augmentation.mp4 (73.3 MB)
  • 5. Sample Data Load with ImageDataGenerator for Augmentation.mp4 (71.0 MB)
  • 15. How to Calculate Number of Parameters in CNN and FCN.mp4 (68.7 MB)
  • 14. How to Calculate Size of Output Layers of CNN and MaxPool.mp4 (61.3 MB)
  • 6. Random Rotation Augmentation.mp4 (55.8 MB)
  • 11. Store Data in Local Directory.mp4 (53.1 MB)
  • 4. What is Data Augmentation [Theory].mp4 (48.6 MB)
  • 7. Random Shift Augmentation.mp4 (45.9 MB)
  • 2. L1, L2 and Early Stopping Regularization.mp4 (44.8 MB)
  • 1. What is Overfitting.mp4 (42.8 MB)
  • 3. How Dropout and Batch Normalization Prevents Overfitting.mp4 (42.7 MB)
  • 19. Regularized CNN Model Building and Training.mp4 (42.4 MB)
  • 10. TensorFlow TFDS and Cats vs Dogs Data Download.mp4 (42.4 MB)
  • 13. Building Baseline CNN Classifier.mp4 (41.6 MB)
  • 16. Model Training and Layers Analysis.mp4 (39.9 MB)
  • 9. All Types of Augmentation at Once.mp4 (32.7 MB)
  • 17. Model Training and Validation Accuracy Plot.mp4 (26.0 MB)
  • 20. Training Log Analysis.mp4 (25.5 MB)
  • 18. Building Dataset for Regularized CNN.mp4 (17.5 MB)
  • 22. CNN Model Visualization.mp4 (14.3 MB)
2. Python for Deep

Description


Description

This comprehensive course covers the latest advancements in deep learning and artificial intelligence using Python. Designed for both beginner and advanced students, this course teaches you the foundational concepts and practical skills necessary to build and deploy deep learning models.

Module 1: Introduction to Python and Deep Learning

Overview of Python programming language
Introduction to deep learning and neural networks

Module 2: Neural Network Fundamentals

Understanding activation functions, loss functions, and optimization techniques
Overview of supervised and unsupervised learning

Module 3: Building a Neural Network from Scratch

Hands-on coding exercise to build a simple neural network from scratch using Python

Module 4: TensorFlow 2.0 for Deep Learning

Overview of TensorFlow 2.0 and its features for deep learning
Hands-on coding exercises to implement deep learning models using TensorFlow

Module 5: Advanced Neural Network Architectures

Study of different neural network architectures such as feedforward, recurrent, and convolutional networks
Hands-on coding exercises to implement advanced neural network models

Module 6: Convolutional Neural Networks (CNNs)

Overview of convolutional neural networks and their applications
Hands-on coding exercises to implement CNNs for image classification and object detection tasks

Module 7: Recurrent Neural Networks (RNNs) [Coming Soon]

Overview of recurrent neural networks and their applications
Hands-on coding exercises to implement RNNs for sequential data such as time series and natural language processing

By the end of this course, you will have a strong understanding of deep learning and its applications in AI, and the ability to build and deploy deep learning models using Python and TensorFlow 2.0. This course will be a valuable asset for anyone looking to pursue a career in AI or simply expand their knowledge in this exciting field.
Who this course is for:

Data scientists, analysts, and engineers who want to expand their knowledge and skills in machine learning.
Developers and programmers who want to learn how to build and deploy machine learning models in a production environment.
Researchers and academics who want to understand the latest developments and applications of machine learning.
Business professionals and managers who want to learn how to apply machine learning to solve real-world problems in their organizations.
Students and recent graduates who want to gain a solid foundation in machine learning and pursue a career in data science or artificial intelligence.
Anyone who is curious about machine learning and wants to learn more about its applications and how it is used in the industry.

Requirements

Basic understanding of programming concepts and mathematics
A laptop or a computer with an internet connection
A willingness to learn and explore the exciting field of deep learning and artificial intelligence

Last Updated 7/2023



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2023 Python for Deep Learning and Artificial Intelligence

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7 GB
seeders:69
leechers:61
2023 Python for Deep Learning and Artificial Intelligence

Torrent hash: 4BCB56EE8F0BA19DA5774EF00191B47718B18A4B