├── .gitignore ├── 01-TF2.0-Overview ├── .gitignore ├── README.md ├── conv_train.py ├── fc_train.py └── features.png ├── 02-AutoGraph ├── README.md ├── graph.gif └── main.py ├── 03-Play-with-MNIST ├── README.md ├── main.py └── mnist.gif ├── 04-Linear-Regression ├── README.md ├── linear_regression.png └── main.py ├── 05-FashionMNIST ├── .gitignore ├── README.md ├── fashion_mnist.png ├── mnist_Seqential_gradient.py ├── mnist_custommodel.py ├── mnist_fit.py └── mnist_matmul.py ├── 06-CIFAR-VGG ├── .gitignore ├── README.md ├── main.py ├── network.py └── vgg16.png ├── 07-Inception ├── .gitignore ├── README.md ├── inception.png └── main.py ├── 08-ResNet ├── .gitignore ├── README.md ├── main.py └── resnet.jpeg ├── 09-RNN-Sentiment-Analysis ├── .gitignore ├── README.md ├── imdb.png └── main.py ├── 10-ColorBot ├── .gitignore ├── README.md ├── blue.png ├── green.png ├── main.py ├── model.py ├── purple.png ├── red.png ├── shot.png └── utils.py ├── 11-AE ├── README.md ├── autoencoder.png ├── images │ ├── vae_reconstructed_epoch_1.png │ ├── vae_reconstructed_epoch_2.png │ ├── vae_reconstructed_epoch_3.png │ ├── vae_reconstructed_epoch_4.png │ └── vae_reconstructed_epoch_5.png └── main.py ├── 12-VAE ├── README.md ├── images │ ├── vae_reconstructed_epoch_1.png │ ├── vae_reconstructed_epoch_2.png │ ├── vae_reconstructed_epoch_3.png │ ├── vae_reconstructed_epoch_4.png │ ├── vae_reconstructed_epoch_5.png │ ├── vae_reconstructed_epoch_6.png │ ├── vae_reconstructed_epoch_7.png │ ├── vae_reconstructed_epoch_8.png │ ├── vae_reconstructed_epoch_9.png │ ├── vae_sampled_epoch_1.png │ ├── vae_sampled_epoch_2.png │ ├── vae_sampled_epoch_3.png │ ├── vae_sampled_epoch_4.png │ ├── vae_sampled_epoch_5.png │ ├── vae_sampled_epoch_6.png │ ├── vae_sampled_epoch_7.png │ ├── vae_sampled_epoch_8.png │ └── vae_sampled_epoch_9.png ├── main.py └── variational_autoencoder.png ├── 13-DCGAN ├── .gitignore ├── README.md ├── dcgan.png ├── gan.py └── main.py ├── 14-Pixel2Pixel ├── .gitignore ├── README.md ├── gd.py ├── main.py └── pix2pix.jpg ├── 15-CycleGAN ├── .gitignore ├── README.md ├── cyclegan.jpg ├── images │ ├── generated_0.png │ └── generated_40.png ├── main.py └── model.py ├── 16-fasterRCNN ├── .gitignore ├── LICENSE ├── README.md ├── detection │ ├── core │ │ ├── anchor │ │ │ ├── anchor_generator.py │ │ │ └── anchor_target.py │ │ ├── bbox │ │ │ ├── bbox_target.py │ │ │ ├── geometry.py │ │ │ └── transforms.py │ │ └── loss │ │ │ └── losses.py │ ├── datasets │ │ ├── coco.py │ │ ├── data_generator.py │ │ ├── transforms.py │ │ └── utils.py │ ├── models │ │ ├── backbones │ │ │ └── resnet.py │ │ ├── bbox_heads │ │ │ └── bbox_head.py │ │ ├── detectors │ │ │ ├── faster_rcnn.py │ │ │ └── test_mixins.py │ │ ├── necks │ │ │ └── fpn.py │ │ ├── roi_extractors │ │ │ └── roi_align.py │ │ └── rpn_heads │ │ │ └── rpn_head.py │ └── utils │ │ └── misc.py ├── image_demo_ckpt.png ├── image_demo_random.png ├── inspect_model.ipynb ├── inspect_model.py ├── roi_test.py ├── shot.png ├── tf.image.crop_and_resize_test.png ├── train_model.ipynb ├── train_model.py └── visualize.py ├── 17-A2C ├── README.md ├── a2c.py ├── cartpole.gif └── reward.png ├── 18-GPT ├── README.md ├── architecture.png └── model.py ├── 19-BERT ├── README.md ├── __init__.py ├── architecture.png ├── bert.py ├── embedding_similarity │ ├── __init__.py │ └── embeddings.py ├── layer_normalization │ ├── __init__.py │ └── layer_normalization.py ├── layers │ ├── __init__.py │ ├── conv.py │ ├── embedding.py │ ├── extract.py │ ├── inputs.py │ ├── masked.py │ └── pooling.py ├── loader.py ├── main.py ├── multi_head_attention │ ├── __init__.py │ ├── multi_head.py │ └── multi_head_attention.py ├── pointwise_feedforward │ ├── __init__.py │ └── feed_forward.py ├── position_embedding │ ├── __init__.py │ ├── pos_embd.py │ └── trig_pos_embd.py ├── self_attention │ ├── __init__.py │ ├── scaled_dot_attention.py │ ├── seq_self_attention.py │ └── seq_weighted_attention.py ├── tokenizer.py └── transformer │ ├── __init__.py │ ├── gelu.py │ └── transformer.py ├── 20-GCN ├── README.MD ├── config.py ├── inits.py ├── layers.py ├── metrics.py ├── models.py ├── nohup.out ├── res │ └── screen.png ├── train.py └── utils.py ├── 21-CN-EN-Translation-BERT ├── Readme.md ├── attention.py ├── attlayer.py ├── bert_train.py ├── bertmodel.py ├── test.py ├── tokenizer.py ├── transformer.py ├── transformer_train.py └── utils.py ├── README.md ├── res ├── TF_QR_163.png ├── ai101.jpg ├── cover.png ├── tensorflow-2.0.gif ├── tensorflow-2.0.jpg └── weibo.jpg └── 深度学习与TensorFlow入门实战-源码和PPT ├── 0-课程介绍 ├── cover.png ├── 深度学习与TF.png ├── 版权声明.png ├── 答疑群-441082251.png └── 请学员务必加群答疑！！！.txt ├── Readme.md ├── lesson01-初见TensorFlow2.0 ├── autograd.py ├── gpu_accelerate.py ├── 初见TensorFlow2.0.pdf ├── 版权声明.png ├── 答疑群-441082251.png └── 请学员务必加群答疑！！！.txt ├── lesson02-开发环境准备 ├── test.py ├── 开发环境安装-极简版.pdf ├── 开发环境安装-标准版.pdf ├── 版权声明.png ├── 答疑群-441082251.png └── 请学员务必加群答疑！！！.txt ├── lesson03-回归问题 ├── 回归问题.pdf ├── 版权声明.png ├── 答疑群-441082251.png └── 请学员务必加群答疑！！！.txt ├── lesson04-回归问题实战 ├── data.csv ├── linear_regression.py ├── 回归实战.pdf ├── 版权声明.png ├── 答疑群-441082251.png └── 请学员务必加群答疑！！！.txt ├── lesson05-手写数字问题 ├── 手写数字问题.pdf ├── 版权声明.png ├── 答疑群-441082251.png └── 请学员务必加群答疑！！！.txt ├── lesson06-手写数字识别初体验 ├── main.py ├── 手写数字问题体验.pdf ├── 版权声明.png ├── 答疑群-441082251.png └── 请学员务必加群答疑！！！.txt ├── lesson07-数据类型 ├── 代码量较少，同学们自己动手练习.txt ├── 数据类型.pdf ├── 版权声明.png ├── 答疑群-441082251.png └── 请学员务必加群答疑！！！.txt ├── lesson08-创建Tensor ├── 代码量较少，同学们自己动手练习.txt ├── 创建Tensor.pdf ├── 版权声明.png ├── 答疑群-441082251.png └── 请学员务必加群答疑！！！.txt ├── lesson09-索引与切片 ├── 代码量较少，同学们自己动手练习.txt ├── 索引与切片-1.pdf └── 索引与切片-2.pdf ├── lesson10-维度变换 └── 维度变换.pdf ├── lesson11-Broadcasting └── Broadcasting.pdf ├── lesson12-数学运算 └── 数学运算.pdf ├── lesson13-前向传播（张量）-实战 ├── forward.py ├── 前向传播.pdf ├── 答疑群-926107229.png └── 请学员务必加群答疑！！！.txt ├── lesson14-合并与分割 └── 合并与分割.pdf ├── lesson15-数据统计 └── 数据统计.pdf ├── lesson16-张量排序 ├── topk.py └── 张量排序.pdf ├── lesson17-填充与复制 └── 填充与复制.pdf ├── lesson18-数据限幅 ├── main.py └── 张量限幅.pdf ├── lesson19-高阶OP ├── meshgrid.py └── 高阶特性.pdf ├── lesson20-数据加载 └── 数据加载.pdf ├── lesson21-测试（张量）-实战 ├── forward.py ├── mnist_tensor.py └── 测试（张量）实战.pdf ├── lesson22-全连接层 ├── mlp.py └── 全接连层.pdf ├── lesson23-输出方式 └── 输出方式.pdf ├── lesson24-误差计算 ├── loss.py └── 误差计算.pdf ├── lesson25-梯度计算 ├── 0.梯度下降-简介.pdf ├── 2.常见函数的梯度.pdf ├── 2nd_derivative.py ├── 3.激活函数及其梯度.pdf ├── 4.损失函数及其梯度.pdf ├── 5.单输出感知机梯度.pdf ├── 6.多输出感知机梯度.pdf ├── 7.链式法则.pdf ├── 8.多层感知机梯度.pdf ├── chain_rule.py ├── crossentropy_loss.py ├── mse_grad.py ├── multi_output_perceptron.py ├── sigmoid_grad.py └── single_output_perceptron.py ├── lesson26-优化方法 ├── himmelblau.py └── 函数优化实战.pdf ├── lesson27-书写数字问题（层）-实战 ├── MNIST实战.pdf ├── fashionmnist_layer.py ├── 答疑群-926107229.png └── 请学员务必加群答疑！！！.txt ├── lesson28-可视化 ├── main.py └── 可视化.pdf ├── lesson30-Keras高层API ├── 1.Metrics.pdf ├── 2.Compile&Fit.pdf ├── 3.自定义层.pdf ├── compile_fit.py ├── layer_model.py └── metrics.py ├── lesson31-Keras模型保存与加载 ├── model.h5 ├── save_load_model.py ├── save_load_weight.py └── 模型加载与保存.pdf ├── lesson32-Keras实战 ├── Keras实战CIFAR10.pdf ├── keras_train.py ├── 答疑群-926107229.png └── 请学员务必加群答疑！！！.txt ├── lesson33-过拟合与欠拟合 └── 过拟合与欠拟合.pdf ├── lesson34-交叉验证 ├── compile_fit.py ├── train_evalute_test.py └── 交叉验证.pdf ├── lesson35-Regularization ├── Regularization.pdf └── regularization.py ├── lesson36-动量与学习率 └── 学习率与动量.pdf ├── lesson37-Early Stopping, Dropout ├── dropout.py └── misc.pdf ├── lesson38-卷积神经网络 ├── 什么是卷积.pdf └── 卷积神经网络.pdf ├── lesson39-池化与采样 └── 池化与采样.pdf ├── lesson40-CIFAR与VGG实战 ├── CIFAR与VGG实战.pdf ├── cifar100_train.py ├── 答疑群-926107229.png └── 请学员务必加群答疑！！！.txt ├── lesson41-经典卷积网络 └── 经典卷积网络.pdf ├── lesson42-BatchNorm ├── BatchNorm.pdf └── main.py ├── lesson43-ResNet ├── ResNet与DenseNet.pdf ├── ResNet实战.pdf ├── resnet.py └── resnet18_train.py ├── lesson44-循环神经网络 ├── 循环神经网络.pdf └── 时间序列表示.pdf ├── lesson45-RNN实战 ├── RNN Layer使用.pdf ├── sentiment_analysis_cell.py ├── sentiment_analysis_layer.py └── 情感分类实战.pdf ├── lesson46-LSTM ├── LSTM.pdf └── 梯度弥散与梯度爆炸.pdf ├── lesson47-LSTM实战 ├── LSTM实战.pdf ├── gru_sentiment_analysis_cell.py ├── gru_sentiment_analysis_layer.py ├── lstm_sentiment_analysis_cell.py └── lstm_sentiment_analysis_layer.py ├── lesson48-AutoEncoders └── AutoEncoders.pdf ├── lesson49-VAE实战 ├── AE实战.pdf ├── autoencoder.py ├── vae.py ├── 答疑群-926107229.png └── 请学员务必加群答疑！！！.txt ├── lesson50-GAN └── GAN.pdf ├── lesson51-WGAN实战 ├── GAN实战.pdf ├── dataset.py ├── gan.py ├── gan_train.py ├── wgan.py ├── wgan_train.py ├── 答疑群-926107229.png └── 请学员务必加群答疑！！！.txt ├── lesson52-自定义数据集和迁移学习 ├── pokemon.py ├── resnet.py ├── train_scratch.py ├── train_transfer.py └── 宝可梦数据集.pdf ├── lesson53-YOLO目标检测 ├── YOLO.pdf ├── YOLO实战.pdf └── 代码需购买课程并加群下载(网易云课堂).txt └── lesson选看-人工智能发展史 └── 人工智能发展史.pdf /.gitignore: -------------------------------------------------------------------------------- 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