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Deep Learning Zero To Hero - Hands-On With Python - OneDDL - 01-15-2024 Free Download Deep Learning Zero To Hero - Hands-On With Python Published 1/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 6.05 GB | Duration: 10h 56m Learn Deep learning practically from scratch using Python What you'll learn How to build artificial neural networks Architectures of feedforward and convolutional networks The calculus and code of gradient descent Learn Python from scratch (no prior coding experience necessary) Requirements Basic Machine learning concepts and Python. Description Deep Learning is a new part of Machine Learning, which has been introduced with the objective of moving Machine Learning closer to Artificial Intelligence. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. Through this training we are going to learn and apply concepts of deep learning with live projects.The course includes the following;•Prediction in Structured/Tabular Data•Recommendation•Image Classification•Image Segmentation•Object Detection•Style Transfer•Super Resolution•Sentiment Analysis•Text Generation•Time Series (Sequence) Prediction•Machine Translation•Speech Recognition•Question Answering•Text Similarity•Image Captioning•Image Generation•Image to Image TranslationWe will be learning the followings:The theory and math underlying deep learningHow to build artificial neural networksArchitectures of feedforward and convolutional networksBuilding models in PyTorchThe calculus and code of gradient descentFine-tuning deep network modelsLearn Python from scratch (no prior coding experience necessary)How and why autoencoders workHow to use transfer learningImproving model performance using regularization Overview Section 1: Deep Learning ZERO To HERO - Hands-On With Python Lecture 1 Introduction to Hands on Deeplearning Lecture 2 What is Machine Learning Lecture 3 Popular ML Methods Lecture 4 What is Deep Learning Lecture 5 Applications of Deeplearning Lecture 6 Recommendations Lecture 7 Basic Concept of Deeplearning Lecture 8 Perception Lecture 9 Neural Network Lecture 10 Universal Approximations Theorem Lecture 11 Deep Neural Network Lecture 12 Deep Neural Network Continue Lecture 13 Getting Started Lecture 14 Where to write Code Lecture 15 Jupiter Notebook Lecture 16 Google Colab Lecture 17 Pytorch Lecture 18 Tensors Lecture 19 Tensors Continue Lecture 20 Gradients Lecture 21 MNIST Example Lecture 22 Check Sample Lecture 23 Hidden Layer Lecture 24 Interface on a Digit Lecture 25 Transfer-Learning-Overview Lecture 26 What is Transfer Learning Lecture 27 CS231n Convolutional Neural Networks Lecture 28 Download Dataset Lecture 29 Transform the Data Lecture 30 Visualize the Data Lecture 31 Define the Model Lecture 32 Add a Few Final Layers Lecture 33 Train the Model Lecture 34 Test the Model Lecture 35 What About CIFAR Lecture 36 Image Classifier on Cifar 10 Dataset Lecture 37 Download and Load Our Dataset Lecture 38 Train and Test Dataset Lecture 39 Define Our Neural Network Lecture 40 Working on Image Lecture 41 Input and Output Lecture 42 Define Our Loss Function Lecture 43 Train Data in Enumerate Lecture 44 Train Data in Enumerate Continue Lecture 45 Test the Neural Network on the Test Image Lecture 46 Intro to Text Classifier Lecture 47 Text Classification Using CNN Lecture 48 Prepare the Data Lecture 49 Build the Model Lecture 50 Build the Model Coninue Lecture 51 More on Build the Model Lecture 52 Define a Loss Function Lecture 53 Define a Loss Function Continue Lecture 54 More on Define a Loss Function Lecture 55 Evaluate or Test the Model Lecture 56 Intro to Text Generation Lecture 57 Text Generation-Transformers Lecture 58 Text Generation-Transformers Continue Lecture 59 Transformers-Architectures Lecture 60 Transformers-Architectures Cintinue Lecture 61 Word-Generation Lecture 62 Word-Generation Continue Lecture 63 Text-Generation Lecture 64 Intro to Text Translation Lecture 65 Loading-Data Lecture 66 Preparing-Data Lecture 67 Encoder-Attention Part 1 Lecture 68 Encoder-Attention Part 2 Lecture 69 Encoder-Attention Part 3 Lecture 70 Decoder Lecture 71 Train-Eval-Functions Lecture 72 Train-Eval-Functions Continue Lecture 73 Training-Fixes Lecture 74 Training-Evaluation Lecture 75 Prediction-Tabular-Data Part 1 Lecture 76 Prediction-Tabular-Data Part 2 Lecture 77 Prediction-Tabular-Data Part 3 Lecture 78 Prediction-Tabular-Data Part 4 Lecture 79 Collaborative Filtering Lecture 80 Collaborative Filtering Continue Lecture 81 Other Recommendation Approaches Aspiring Data Scientists and AI/Machine Learning/Deep Learning Engineers Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live No Password - Links are Interchangeable |