▲
0
0

report

Deep Learning : Image Classification with Tensorflow in 2023

seeders: 12
leechers: 5
Download torrent
Added 3 years ago by tutsnode in Other
Download Fast Safe Anonymous
movies, software, shows...

Files

Deep Learning : Image Classification with Tensorflow in 2023 (Size: 15 GB)
  0 1.1 MB
  1 - Welcome.mp4 32 MB
  10 - Sparse Tensors.mp4 18 MB
  11 - String Tensors.mp4 23 MB
  12 - Variables.mp4 44.9 MB
  13 - Understanding the Task.mp4 30.5 MB
  14 - Data Preparation.mp4 276.3 MB
  15 - Linear Regression Model.mp4 105.1 MB
  16 - Error Sanctioning.mp4 95.2 MB
  17 - Training and Optimization.mp4 116.7 MB
  18 - Performance Measurement.mp4 22 MB
  19 - Validation and Testing.mp4 135.7 MB
  2 - General Introduction.mp4 202.1 MB
  20 - Corrective Measures.mp4 195.8 MB
  21 - Understanding the Task.mp4 63.8 MB
  22 - Data Preparation.mp4 160.6 MB
  23 - Data Visualization.mp4 16.8 MB
  24 - Data Processing.mp4 54.5 MB
  25 - How and Why Convolutional Neural Networks Work.mp4 348.7 MB
  26 - Building ConvNets with TensorFlow.mp4 44.7 MB
  27 - Binary Crossentropy Loss.mp4 47.1 MB
  28 - Training.mp4 113.7 MB
  29 - Model Evaluation and Testing.mp4 39.7 MB
  3 - Basics.mp4 43.5 MB
  30 - Loading and Saving tensorflow models to gdrive.mp4 128.9 MB
  31 - Functional API.mp4 138.4 MB
  32 - Model Subclassing.mp4 119.6 MB
  33 - Custom Layers.mp4 135.7 MB
  34 - PrecisionRecallAccuracy.mp4 211.4 MB
  35 - Confusion Matrix.mp4 62.3 MB
  36 - ROC curve.mp4 50.2 MB
  37 - Callbacks with TensorFlow.mp4 217.4 MB
  38 - Learning Rate Scheduling.mp4 136.8 MB
  39 - Model Checkpointing.mp4 61.8 MB
  4 - Initialization and Casting.mp4 406.5 MB
  40 - Mitigating Overfitting and Underfitting with Dropout Regularization.mp4 202.1 MB
  41 - Data augmentation with TensorFlow using tfimage and Keras Layers.mp4 424 MB
  42 - Mixup Data augmentation with TensorFlow 2 with intergration in tfdata.mp4 161.9 MB
  43 - Cutmix Data augmentation with TensorFlow 2 and intergration in tfdata.mp4 344.2 MB
  44 - Albumentations with TensorFlow 2 and PyTorch for Data augmentation.mp4 617.5 MB
  45 - Custom Loss and Metrics in TensorFlow 2.mp4 176.1 MB
  46 - Eager and Graph Modes in TensorFlow 2.mp4 88.7 MB
  47 - Custom Training Loops in TensorFlow 2.mp4 234.9 MB
  48 - Log data.mp4 287.2 MB
  49 - view model graphs.mp4 21.5 MB
  5 - Indexing.mp4 77.6 MB
  50 - hyperparameter tuning.mp4 195 MB
  51 - Profiling and other visualizations with Tensorboard.mp4 69.2 MB
  52 - Experiment Tracking.mp4 469.6 MB
  53 - Hyperparameter Tuning with Weights and Biases and TensorFlow 2.mp4 222.7 MB
  54 - Dataset Versioning with Weights and Biases and TensorFlow 2.mp4 329.4 MB
  55 - Model Versioning with Weights and Biases and TensorFlow 2.mp4 137.4 MB
  56 - data preparation.mp4 225.5 MB
  57 - Modeling and Training.mp4 371.9 MB
  58 - Data augmentation.mp4 142.2 MB
  59 - Tensorflow records.mp4 293.6 MB
  6 - Maths Operations.mp4 215 MB
  60 - Alexnet.mp4 183.3 MB
  61 - vggnet.mp4 116.4 MB
  62 - resnet.mp4 351.8 MB
  63 - coding resnet.mp4 180.3 MB
  64 - mobilenet.mp4 206.8 MB
  65 - efficientnet.mp4 189.2 MB
  66 - Pretrained Models.mp4 163.5 MB
  67 - Finetuning.mp4 112.2 MB
  68 - visualizing intermediate layers.mp4 158.1 MB
  69 - gradcam method.mp4 226.8 MB
  7 - Linear Algebra Operations.mp4 371.5 MB
  70 - Ensembling.mp4 45.2 MB
  71 - Class imbalance.mp4 100.6 MB
  72 - Understanding VITs.mp4 422 MB
  73 - Building VITs from scratch.mp4 398.6 MB
  74 - Finetuning Huggingface VITs.mp4 206.6 MB
  75 - Model Evaluation with Wandb.mp4 140.2 MB
  76 - Data efficient Transformers.mp4 72.8 MB
  77 - Swin Transformers.mp4 192.2 MB
  78 - Conversion from tensorflow to Onnx Model.mp4 205.4 MB
  79 - Understanding quantization.mp4 268.2 MB
  8 - Common Methods.mp4 299.1 MB
  80 - Practical quantization of Onnx Model.mp4 65 MB
  81 - Quantization Aware training.mp4 160.5 MB
  82 - Conversion to tensorflowlite model.mp4 154.7 MB
  83 - How APIs work.mp4 127.9 MB
  84 - Building API with Fastapi.mp4 674.9 MB
  85 - Deploying API to the Cloud.mp4 100.2 MB
  86 - Load testing API.mp4 106.3 MB
  9 - RaggedTensors.mp4 78.5 MB
  TutsNode.net.txt 102.4 B
  [TGx]Downloaded from torrentgalaxy.to .txt 614.4 B
  1 501.2 KB
  2 367.2 KB
  3 2 MB
  4 47.6 KB
  5 1.5 MB
  6 1.4 MB
  7 115 KB
  8 508.2 KB
  9 227.4 KB
  10 1.3 MB
  11 1.8 MB
  12 563.9 KB
  13 952.6 KB
  14 364.4 KB
  15 795.1 KB
  16 1.7 MB
  17 1.8 MB
  18 1.1 MB
  19 1.2 MB
  20 561.8 KB
  21 1.3 MB
  22 663.6 KB
  23 981.7 KB
  24 632.6 KB
  25 1.2 MB
  26 1.4 MB
  27 585.2 KB
  28 1.9 MB
  29 1.9 MB
  30 161.6 KB
  31 1 MB
  32 1.8 MB
  33 840.8 KB
  34 728.4 KB
  35 1.7 MB
  36 1.9 MB
  37 560.2 KB
  38 147.2 KB
  39 1.4 MB
  40 1.5 MB
  41 1.9 MB
  42 1.3 MB
  43 1.8 MB
  44 1.8 MB
  45 1.6 MB
  46 582.7 KB
  47 1.2 MB
  48 313.8 KB
  49 342.8 KB
  50 1.1 MB
  51 82.4 KB
  52 455.6 KB
  53 1.3 MB
  54 1.6 MB
  55 276.9 KB
  56 1.8 MB
  57 1.7 MB
  58 887 KB
  59 1.4 MB
  60 1.8 MB
  61 860 KB
  62 1.3 MB
  63 1.5 MB
  64 442.6 KB
  65 1.2 MB
  66 867.2 KB
  67 1 MB
  68 247.9 KB
  69 1.7 MB
  70 205.3 KB
  71 1.5 MB
  72 1.8 MB
  73 872.5 KB
  74 768.4 KB
  75 1.1 MB
  76 1.3 MB
  77 468.4 KB
  78 317 KB
  79 2 MB
  80 1.5 MB
  82 45.4 KB
  83 526.7 KB
  84 2 MB
  ▲ 172 total files

Description


Description

Image classification models find themselves in different places today, like farms, hospitals, industries, schools, and highways,…

With the creation of much more efficient deep learning models from the early 2010s, we have seen a great improvement in the state of the art in the domain of image classification.

In this course, we shall take you on an amazing journey in which you’ll master different concepts with a step-by-step approach. We shall start by understanding how image classification algorithms work, and deploying them to the cloud while observing best practices. We are going to be using Tensorflow 2 (the world’s most popular library for deep learning, built by Google) and Huggingface

You will learn:

The Basics of Tensorflow (Tensors, Model building, training, and evaluation)
Deep Learning algorithms like Convolutional neural networks and Vision Transformers
Evaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)
Mitigating overfitting with Data augmentation
Advanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, Tensorboard
Machine Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)
Binary Classification with Malaria detection
Multi-class Classification with Human Emotions Detection
Transfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet)
Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)

If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!

This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.

Enjoy!!!
Who this course is for:

Beginner Python Developers curious about Applying Deep Learning for Computer vision
Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood
Anyone who wants to master deep learning fundamentals and also practice deep learning for image classification using best practices in TensorFlow.
Computer Vision practitioners who want to learn how state of art image classification models are built and trained using deep learning.
Anyone wanting to deploy image classification Models
Learners who want a practical approach to Deep learning for image classification

Requirements

Basic Knowledge of Python
Access to an internet connection, as we shall be using Google Colab (free version)

Last Updated 2/2023

Related Torrents

torrent name size uploader age seed leech
Coursera - Advanced Deep Learning Architectures Specialization Posted by freecoursewb in Other
4 GB freecoursewb 3 months 12 10
Full-Stack Deep Learning with Python (2026) Posted by freecoursewb in Other
498 MB freecoursewb 4 months 16 2
Udemy - Deep Learning - Convolutional Neural Networks in Python Posted by freecoursewb in Other
4 GB freecoursewb 6 months 24 10
Deep Learning with Python, Third Edition, Video Edition Posted by freecoursewb in Other
2.5 GB freecoursewb 8 months 14 3
Udemy - Python for Deep Learning - Build Neural Networks in Python Posted by freecoursewb in Other
785.3 MB freecoursewb 9 months 13 1