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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-masterclass-with-tensorflow-2-over-15-projects/
课程评论：没有评论
课程名称：《深度学习大师班与 TensorFlow 2：20 多个项目》 课程简介：深度学习是当今计算机科学中最受欢迎的领域之一，具有广泛的应用。自2010年代初推出更高效的深度学习模型以来，计算机视觉、自然语言处理、图像生成和信号处理等领域的技术水平得到了显著提升。深度学习工程师的需求激增，相关专家的薪资也相对较高，因其在职场中的价值。然而，初学者入门并不容易，信息量庞大且很多内容已过时，且并未充分考虑初学者的需求。 本课程将带您踏上一次令人振奋的旅程，逐步掌握不同的深度学习概念，并采用项目驱动的学习方法。您将使用 TensorFlow 2（全球最流行的深度学习库，由谷歌开发）和 Huggingface。课程将从构建简单模型（例如：汽车价格预测的线性回归模型、电影评论文本分类器、疟疾预测的二分类器）开始，逐步深入到更高级的模型（例如：YOLO 目标检测模型、GPT2 歌词生成模型以及 GANs 图像生成）。 完成本课程后，您将具备为大科技公司开发现代深度学习解决方案所需的技能。 您将学习内容包括： - TensorFlow 基础（张量、模型构建、训练和评估） - 深度学习算法，如卷积神经网络（CNN）和视觉 Transformer - 分类模型评估（精确度、召回率、准确率、F1 分数、混淆矩阵、ROC 曲线） - 通过数据增强来减轻过拟合 - 高级 TensorFlow 概念（自定义损失和指标、急切和图模式、自定义训练循环、Tensorboard） - 机器学习操作（MLOps）与 Weights and Biases - 疟疾检测的二分类 - 人类情感检测的多分类 - 使用现代卷积神经网络和视觉 Transformers 的迁移学习 - 使用 YOLO 进行目标检测 - 使用 UNet 进行图像分割 - 使用 Csrnet 进行人数计数 - 模型部署技术（蒸馏、ONNX 格式、量化、FastAPI、Heroku Cloud） - 变分自编码器生成数字 - 生成对抗网络（GAN）生成面孔 - 自然语言处理的文本预处理 - 使用 RNN、注意力模型、Transformer 和 CNN 的深度学习算法 - 使用 RNNs、Transformers 和 Huggingface Transformers 进行情感分析 - 使用 RNNs、注意力、Transformers 和 Huggingface Transformers 进行机器翻译 - 使用 Huggingface Transformers 的个体分类、命名实体识别、神经机器翻译等任务 - 使用 GPT2 和 T5 进行歌词生成和语法错误修正 - 语音识别等多种任务 如果您希望在职业生涯中迈出进一步的步伐，本课程正是为您量身定制的，我们非常期待帮助您实现目标！本课程由 Neuralearn 提供，与 Neuralearn 的其他课程一样，我们非常重视反馈。您在论坛上的评论和问题将帮助我们改进本课程。请随时在论坛上提问，我们将尽力在最短的时间内回复您。 祝您学习愉快！
Deep Learning is one of the most popular fields in computer science today. It has applications in many and very varied domains. With the publishing of much more efficient deep learning models in the early 2010s, we have seen a great improvement in the state of the art in domains like Computer Vision, Natural Language Processing, Image Generation, and Signal Processing. The demand for Deep Learning engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn't easy. There's so much information out there, much of which is outdated and many times don't take the beginners into consideration:(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world's most popular library for deep learning, and built by Google) and Huggingface. We shall start by understanding how to build very simple models (like Linear regression models for car price prediction, text classifiers for movie reviews, binary classifiers for malaria prediction) using Tensorflow and Huggingface transformers, to more advanced models (like object detection models with YOLO, lyrics generator model with GPT2 and Image generation with GANs)After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep-learning solutions that big tech companies encounter.You will learn:The Basics of Tensorflow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision TransformersEvaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)Mitigating overfitting with Data augmentationAdvanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, TensorboardMachine 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 DetectionTransfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet) and Vision Transformers (VITs)Object Detection with YOLO (You Only Look Once)Image Segmentation with UNetPeople Counting with Csrnet Model Deployment (Distillation, Onnx format, Quantization, Fastapi, Heroku Cloud)Digit generation with Variational AutoencodersFace generation with Generative Adversarial Neural NetworksText Preprocessing for Natural Language Processing.Deep Learning algorithms like Recurrent Neural Networks, Attention Models, Transformers, and Convolutional neural networks.Sentiment analysis with RNNs, Transformers, and Huggingface Transformers (Deberta)Transfer learning with Word2vec and modern Transformers (GPT, Bert, ULmfit, Deberta, T5...)Machine translation with RNNs, attention, transformers, and Huggingface Transformers (T5)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)Intent Classification with Deberta in Huggingface transformersNamed Entity Relation with Roberta in Huggingface transformersNeural Machine Translation with T5 in Huggingface transformersExtractive Question Answering with Longformer in Huggingface transformersE-commerce search engine with Sentence transformersLyrics Generator with GPT2 in Huggingface transformersGrammatical Error Correction with T5 in Huggingface transformersElon Musk Bot with BlenderBot in Huggingface transformersSpeech recognition with RNNsIf 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!!!
