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2023 Python for Deep Learning and Artificial Intelligence

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2023 Python for Deep Learning and Artificial Intelligence (Size: 7 GB)
  0 688.4 KB
  1. Classical Machine Learning Introduction.mp4 32 MB
  1. Introduction to Computer Vision with Deep Learning.mp4 43 MB
  1. Introduction to NLP.mp4 22.6 MB
  1. Jupyter Notebook Introduction.mp4 103.1 MB
  1. Machine Learning Process Introduction.mp4 38 MB
  1. Overview of Image Classification using CNNs.mp4 44.1 MB
  1. Python Introduction Part 1.mp4 33.6 MB
  1. Transfer Learning Introduction.mp4 57.4 MB
  1. What is Neuron.mp4 20.9 MB
  1 978.9 KB
  1. What is Convolutional Neural Network.mp4 64.2 MB
  1. What is Overfitting.mp4 42.8 MB
  1.1 python-for-deep-learning-and-ai.zip 74.7 MB
  2 767.2 KB
  3 713.5 KB
  4 939.7 KB
  5 683.9 KB
  10. CNN Parameter Calculations Part 2.mp4 43.2 MB
  10. Code Along in Python Part 1.mp4 34.4 MB
  10. Data Visualization Part 2.mp4 107.3 MB
  10. Deep Learning Tools.mp4 31.8 MB
  10. LeNet-5 Architecture Explained.mp4 71.1 MB
  10. Pair Plot.mp4 41.9 MB
  10. Seaborn Introduction Part 2.mp4 59.7 MB
  10. TensorFlow TFDS and Cats vs Dogs Data Download.mp4 42.4 MB
  10. Train Model with TFDS Data Without Saving Locally Part 2.mp4 38.5 MB
  11. AlexNet Architecture Explained.mp4 98.7 MB
  11. CNN Parameter Calculations Part 3.mp4 61.4 MB
  11. Code Along in Python Part 2.mp4 59.1 MB
  11. Data Preprocessing.mp4 36.4 MB
  11. MLops with AWS.mp4 21.6 MB
  11. Store Data in Local Directory.mp4 53.1 MB
  11. Train Test Split.mp4 8.7 MB
  11. import VGG16 from Keras.mp4 51 MB
  12. Code Along in Python Part 3.mp4 44.1 MB
  12. Data Augmentation for Training.mp4 25.6 MB
  12. GoogLeNet (Inception V1) Architecture Explained.mp4 68.4 MB
  12. Import Neural Networks APIs.mp4 37.1 MB
  12. Load Dataset for Baseline Classifier.mp4 82.9 MB
  12. Model Training.mp4 58.9 MB
  12. TF-IDF Vectorization.mp4 34.7 MB
  13. Building Baseline CNN Classifier.mp4 41.6 MB
  13. Code Along in Python Part 4.mp4 66.7 MB
  13. How to Get Input Shape and Class Weights.mp4 21.2 MB
  13. Make CNN Model with VGG16 Transfer Learning.mp4 63.9 MB
  13. Model Evaluation and Prediction on Real Data.mp4 22.3 MB
  13. Model Load and Save.mp4 32.1 MB
  13. RestNet Architecture Explained.mp4 56.8 MB
  14. How to Calculate Size of Output Layers of CNN and MaxPool.mp4 61.3 MB
  14. Image Class Prediction.mp4 52.3 MB
  14. MobileNet Architecture Explained.mp4 121.3 MB
  14. Model Load and Store.mp4 22.1 MB
  14. Model Training for Better Accuracy.mp4 23.3 MB
  14. Neural Network Model Building.mp4 60.9 MB
  15. EfficientNet Architecture Explained.mp4 104.3 MB
  15. How to Calculate Number of Parameters in CNN and FCN.mp4 68.7 MB
  15. Model Summary Explanation.mp4 48.8 MB
  15. Train Any Model for Transfer Learning.mp4 63.3 MB
  16. Model Training and Layers Analysis.mp4 39.9 MB
  16. Model Training.mp4 56.3 MB
  16. Save and Load Model with Class Names.mp4 40.4 MB
  17. Model Evaluation.mp4 16.1 MB
  17. Model Training and Validation Accuracy Plot.mp4 26 MB
  17. Online Prediction of Flowers Classes.mp4 96.9 MB
  18. Building Dataset for Regularized CNN.mp4 17.5 MB
  18. Model Save and Load.mp4 23.6 MB
  19. Prediction on Real-Life Data.mp4 50.9 MB
  19. Regularized CNN Model Building and Training.mp4 42.4 MB
  2. 5 Steps of Computer Vision Model Building.mp4 27.7 MB
  2. Introduction to TensorFlow Datasets (TFDS).mp4 74.6 MB
  2. L1, L2 and Early Stopping Regularization.mp4 44.8 MB
  2. Load Flowers Dataset for Classification.mp4 68 MB
  2. Logistic Regression.mp4 34.4 MB
  2. Multi-Layer Perceptron.mp4 55.1 MB
  2. Python Introduction Part 2.mp4 37.8 MB
  2. Types of Machine Learning.mp4 19.3 MB
  2. What are Key NLP Techniques.mp4 39.6 MB
  2. Working Principle of CNN.mp4 80.2 MB
  20. Training Log Analysis.mp4 25.5 MB
  21. Load Model and Do the Prediction.mp4 83.4 MB
  22. CNN Model Visualization.mp4 14.3 MB
  3. Convolutional Filters.mp4 114 MB
  3. Download Flowers Data.mp4 50 MB
  3. Download Humans or Horses Dataset Part 1.mp4 56.2 MB
  3. Fashion MNIST Dataset Download.mp4 63.2 MB
  3. How Dropout and Batch Normalization Prevents Overfitting.mp4 42.7 MB
  3. Overview of NLP Tools.mp4 64.5 MB
  3. Python Introduction Part 3.mp4 34.7 MB
  3. Shallow vs Deep Neural Networks.mp4 13.8 MB
  3. Supervised Learning.mp4 25.5 MB
  3. Support Vector Machine - SVM.mp4 37.7 MB
  4. Activation Function.mp4 40.3 MB
  4. Common Challenges in NLP.mp4 19.1 MB
  4. Decision Tree.mp4 25.5 MB
  4. Download Humans or Horses Dataset Part 2.mp4 76 MB
  4. Fashion MNIST Dataset Analysis.mp4 87.8 MB
  4. Feature Maps.mp4 66.9 MB
  4. Flowers Data Visualization.mp4 48.7 MB
  4. Numpy Introduction Part 1.mp4 40.1 MB
  4. Unsupervised Learning.mp4 43.2 MB
  4. What is Data Augmentation [Theory].mp4 48.6 MB
  5. Bag of Words - The Simples Word Embedding Technique.mp4 27.3 MB
  5. Numpy Introduction Part 2.mp4 36.3 MB
  5. Padding and Strides.mp4 102.3 MB
  5. Preparing Data with Image Data Generator.mp4 51.2 MB
  5. Random Forest.mp4 17.5 MB
  5. Reinforcement Learning.mp4 16.3 MB
  5. Sample Data Load with ImageDataGenerator for Augmentation.mp4 71 MB
  5. Train Test Split for Data.mp4 25.8 MB
  5. Use of Image Data Generator.mp4 73.4 MB
  5. What is Back Propagation.mp4 79.4 MB
  6. Baseline CNN Model Building.mp4 46.4 MB
  6. Data Display in Subplots Matrix.mp4 89.2 MB
  6. L2 Regularization.mp4 38.3 MB
  6. Optimizers in Deep Learning.mp4 52.1 MB
  6. Pandas Introduction.mp4 49.6 MB
  6 287.6 KB
  6. Deep Neural Network Model Building.mp4 36.5 MB
  6. Pooling Layers.mp4 86.5 MB
  6. Random Rotation Augmentation.mp4 55.8 MB
  6. Term Frequency - Inverse Document Frequency (TF-IDF).mp4 20 MB
  6. What is Deep Learning and ML.mp4 30.2 MB
  7. Activation Function.mp4 72.7 MB
  7. CNN Introduction.mp4 53 MB
  7. L1 Regularization.mp4 18.7 MB
  7. Load Spam Dataset.mp4 18.6 MB
  7. Matplotlib Introduction Part 1.mp4 64.8 MB
  7. Model Summary and Training.mp4 65 MB
  7. Steps to Build Neural Network.mp4 64.1 MB
  7 75.5 KB
  7. How to Calculate Number of Parameters in CNN.mp4 63.8 MB
  7. Random Shift Augmentation.mp4 45.9 MB
  7. What is Neural Network.mp4 33.2 MB
  8. Baseline CNN Model Training.mp4 46.3 MB
  8. Building CNN Model.mp4 60.7 MB
  8. Customer Churn Dataset Loading.mp4 26 MB
  8. Discovering Overfitting - Early Stopping.mp4 77.5 MB
  8. Dropout.mp4 32.4 MB
  8. How Deep Learning Process Works.mp4 23.9 MB
  8. Matplotlib Introduction Part 2.mp4 70.1 MB
  8. Model Evaluation.mp4 31.5 MB
  8. Other Types of Data Augmentation.mp4 73.3 MB
  8. Text Preprocessing.mp4 45.8 MB
  8 796.9 KB
  9. All Types of Augmentation at Once.mp4 32.7 MB
  9. Application of Deep Learning.mp4 28.6 MB
  9. CNN Architectures Comparison.mp4 55.6 MB
  9. CNN Parameter Calculation.mp4 44.8 MB
  9. Data Visualization Part 1.mp4 50.2 MB
  9 248.5 KB
  9. Feature Engineering.mp4 33.7 MB
  9. Model Save and Load for Prediction.mp4 44.7 MB
  9. ROC-AUC Curve.mp4 13.5 MB
  9. Seaborn Introduction Part 1.mp4 30.6 MB
  9. Train Model with TFDS Data Without Saving Locally Part 1.mp4 41.3 MB
  TutsNode.net.txt 102.4 B
  [TGx]Downloaded from torrentgalaxy.to .txt 614.4 B
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  ▲ 290 total files

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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