{"id":2074,"date":"2020-05-06T02:41:16","date_gmt":"2020-05-06T02:41:16","guid":{"rendered":"http:\/\/jmcspace.com\/?page_id=2074"},"modified":"2020-05-06T02:41:16","modified_gmt":"2020-05-06T02:41:16","slug":"lop-ai-co-ban","status":"publish","type":"page","link":"https:\/\/jmcspace.com\/index.php\/lop-ai-co-ban\/","title":{"rendered":"L\u1edbp AI c\u01a1 b\u1ea3n"},"content":{"rendered":"\n

KHO\u00c1 H\u1eccC TR\u00cd TU\u1ec6 NH\u00c2N T\u1ea0O (AI) C\u01a0 B\u1ea2N<\/strong><\/h3>
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<\/p><\/div>\n\n\n\n

1. Gi\u1edbi thi\u1ec7u<\/strong><\/h5>\n\n\n\n

Kho\u00e1 \u0111\u00e0o t\u1ea1o mong mu\u1ed1n mang b\u1ea1n \u0111\u1ebfn g\u1ea7n h\u01a1n v\u1edbi cu\u1ed9c c\u00e1ch m\u1ea1ng c\u00f4ng nghi\u1ec7p 4.0, t\u1ea1o c\u01a1 h\u1ed9i \u0111\u1ec3 m\u1ecdi ng\u01b0\u1eddi c\u00f9ng h\u1ecdc h\u1ecfi v\u1ec1 nh\u1eefng b\u01b0\u1edbc ti\u1ebfn c\u00f4ng ngh\u1ec7 hi\u1ec7n nay. T\u1eeb Tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o (AI), Data Science, Blockchain, Robotics, IoT, t\u1edbi nh\u1eefng tr\u00e0o l\u01b0u c\u00f2n ch\u01b0a \u0111\u01b0\u1ee3c bi\u1ebft \u0111\u1ebfn r\u1ed9ng r\u00e3i.<\/p>\n\n\n\n

T\u1eeb n\u1ec1n t\u1ea3ng c\u1ee7a ng\u00e0nh Khoa h\u1ecdc m\u00e1y t\u00ednh, ch\u01b0\u01a1ng tr\u00ecnh s\u1ebd gi\u1edbi thi\u1ec7u v\u00e0 \u0111i s\u00e2u nghi\u00ean c\u1ee9u c\u00e1c thu\u1eadt to\u00e1n trong Machine Learning v\u00e0 Deep Learning. T\u00ecm hi\u1ec3u v\u00e0 tr\u1ea3i nghi\u1ec7m c\u00e1c \u1ee9ng d\u1ee5ng AI qua c\u00e1c b\u00e0i t\u1eadp nh\u1ecf v\u00e0 d\u1ef1 \u00e1n th\u1ef1c t\u1ebf.<\/p>\n\n\n\n

Ch\u01b0\u01a1ng tr\u00ecnh h\u01b0\u1edbng \u0111\u1ebfn \u0111\u00e0o t\u1ea1o k\u1ef9 s\u01b0 h\u1ecdc m\u00e1y (Machine Learning Engineer) v\u1edbi 5 m\u1ee5c ti\u00eau h\u01b0\u1edbng \u0111\u1ebfn cho h\u1ecdc vi\u00ean: (i) \u0110\u1ea3m b\u1ea3o kh\u1ea3 n\u0103ng t\u01b0 duy To\u00e1n h\u1ecdc; (ii) Kh\u1ea3 n\u0103ng l\u1eadp tr\u00ecnh; (iii) Kh\u1ea3 n\u0103ng x\u1eed l\u00fd d\u1eef li\u1ec7u; (iv) N\u1eafm b\u1eaft c\u00e1c thu\u1eadt to\u00e1n trong Machine Learning; (v) Kh\u1ea3 n\u0103ng s\u1eed d\u1ee5ng Machine Learning Frameworks. <\/p>\n\n\n\n

Kho\u00e1 h\u1ecdc t\u1eadp trung ph\u00e1t tri\u1ec3n tr\u1ef1c gi\u00e1c h\u00ecnh h\u1ecdc qua c\u00e1c kh\u00e1i ni\u1ec7m to\u00e1n h\u1ecdc tr\u1eebu t\u01b0\u1ee3ng. C\u00e1c v\u1ea5n \u0111\u1ec1 v\u00e0 thu\u1eadt to\u00e1n trong Machine Leanring v\u00e0 Deep Learning \u0111\u01b0\u1ee3c tr\u00ecnh b\u00e0y tr\u1ef1c quan t\u1eeb kh\u00e1i ni\u1ec7m, \u00fd ngh\u0129a v\u00e0 nguy\u00ean t\u1eafc ho\u1ea1t \u0111\u1ed9ng qua c\u00e1c vi d\u1ee5 minh ho\u1ea1 gi\u00fap h\u1ecdc vi\u00ean d\u1ec5 d\u00e0ng n\u1eafm b\u1eaft, \u0111\u1ebfn c\u00e1c c\u00f4ng th\u1ee9c, m\u00f4 h\u00ecnh To\u00e1n h\u1ecdc, n\u1ec1n t\u1ea3ng ki\u1ebfn th\u1ee9c ph\u00eda d\u01b0\u1edbi h\u1ed7 tr\u1ee3 cho h\u1ecdc vi\u00ean c\u00f3 kh\u1ea3 n\u0103ng t\u00ecm hi\u1ec3u s\u00e2u h\u01a1n.<\/p>\n\n\n\n

Kho\u00e1 h\u1ecdc n\u00e0y s\u1ebd gi\u1edbi thi\u1ec7u c\u00e1c \u1ee9ng d\u1ee5ng th\u1ef1c t\u1ebf c\u1ee7a AI v\u00e0 Machine Learning Frameworks nh\u01b0: TensorFlow, Keras, PyTorch,\u2026 h\u01b0\u1edbng d\u1eabn h\u1ecdc vi\u00ean Step-by-Step th\u1ef1c hi\u1ec7n c\u00e1c b\u00e0i t\u1eadp v\u00e0 d\u1ef1 \u00e1n, gi\u00fap h\u1ecdc vi\u00ean tr\u1ea3i nghi\u1ec7m th\u1ef1c t\u1ebf x\u00e2y d\u1ef1ng c\u00e1c m\u00f4 h\u00ecnh \u1ee9ng d\u1ee5ng AI, n\u00e2ng cao kh\u1ea3 n\u0103ng l\u1eadp tr\u00ecnh, kh\u1ea3 n\u0103ng x\u1eed l\u00fd d\u1eef li\u1ec7u v\u00e0 n\u1eafm b\u1eaft r\u00f5 h\u01a1n c\u00e1c v\u1ea5n \u0111\u1ec1, thu\u1eadt to\u00e1n trong AI.<\/p>\n\n\n\n

2.\u00a0\u0110\u1ed1i t\u01b0\u1ee3ng tham gia<\/strong><\/h5>\n\n\n\n
  • C\u00e1c l\u1eadp tr\u00ecnh vi\u00ean mong mu\u1ed1n t\u00ecm hi\u1ec3u v\u00e0 l\u00e0m ch\u1ee7 c\u00f4ng ngh\u1ec7 AI \u0111\u1ec3 ph\u1ee5c v\u1ee5 cho c\u00f4ng vi\u1ec7c hi\u1ec7n t\u1ea1i c\u0169ng nh\u01b0 \u0111\u1ecbnh h\u01b0\u1edbng c\u00f4ng vi\u1ec7c cho t\u01b0\u01a1ng lai.<\/li>
  • C\u00e1c b\u1ea1n h\u1ecdc sinh, sinh vi\u00ean c\u00f3 nhu c\u1ea7u ti\u1ebfp c\u1eadn c\u00f4ng ngh\u1ec7 AI t\u1eeb s\u1ed1 kh\u00f4ng nh\u01b0ng ch\u01b0a bi\u1ebft b\u1eaft \u0111\u1ea7u nh\u01b0 th\u1ebf n\u00e0o.<\/li>
  • C\u00e1c nh\u00e0 qu\u1ea3n tr\u1ecb, ho\u1ea1ch \u0111\u1ecbnh chi\u1ebfn l\u01b0\u1ee3c cho doanh nghi\u1ec7p theo h\u01b0\u1edbng \u1ee9ng d\u1ee5ng c\u00f4ng ngh\u1ec7 hi\u1ec7n \u0111\u1ea1i, t\u1ed1i \u01b0u ho\u00e1 gi\u00e1 tr\u1ecb trong doanh nghi\u1ec7p.<\/li><\/ul>\n\n\n\n
    3.\u00a0N\u1ed9i dung kho\u00e1 h\u1ecdc<\/strong><\/h5>\n\n\n\n

    N\u1ed9i dung kho\u00e1 h\u1ecdc \u0111\u01b0\u1ee3c chia th\u00e0nh t\u1eebng ph\u1ea7n t\u01b0\u01a1ng \u1ee9ng v\u1edbi c\u00e1c v\u1ea5n \u0111\u1ec1 c\u01a1 b\u1ea3n trong AI:<\/p>\n\n\n\n

    • Gi\u1edbi thi\u1ec7u t\u1ed5ng quan n\u1ec1n t\u1ea3ng To\u00e1n cho AI: Linear Algebra, Probability, Calculus;<\/li>
    • Gi\u1edbi thi\u1ec7u ng\u00f4n ng\u1eef l\u1eadp tr\u00ecnh Python v\u00e0 th\u01b0 vi\u1ec7n Numpy;<\/li>
    • Gi\u1edbi thi\u1ec7u Machine Learning;<\/li>
    • Linear Regression v\u00e0 \u1ee9ng d\u1ee5ng;<\/li>
    • Logistic Regression v\u00e0 \u1ee9ng d\u1ee5ng;<\/li>
    • K-means Clustering v\u00e0 \u1ee9ng d\u1ee5ng;<\/li>
    • Support Vector Machine (SVM) v\u00e0 \u1ee9ng d\u1ee5ng;<\/li>
    • Gi\u1edbi thi\u1ec7u Deep Learning;<\/li>
    • Gi\u1edbi thi\u1ec7u TensorFlow;<\/li>
    • Neural Network v\u00e0 \u1ee9ng d\u1ee5ng;<\/li>
    • Convolutional Neural Networks (CNN) v\u00e0 \u1ee9ng d\u1ee5ng;<\/li>
    • Recurrent Neural Networks (RNNs) v\u00e0 \u1ee9ng d\u1ee5ng<\/li>
    • M\u00f4 h\u00ecnh Long Short-Term Memory (LSTM) v\u00e0 \u1ee9ng d\u1ee5ng;<\/li><\/ul>\n\n\n\n

      \u0110\u1ec0 C\u01af\u01a0NG CHI TI\u1ebeT<\/strong><\/h3>
      <\/div><\/div>

      <\/p><\/div>\n\n\n\n

      1. Gi\u1edbi thi\u1ec7u chung <\/strong><\/h5>\n\n\n\n

      N\u1ed9i dung kho\u00e1 h\u1ecdc \u0111\u01b0\u1ee3c x\u00e2y d\u1ef1ng d\u1ef1a tr\u00ean kho\u00e1 Machine Learning c\u1ee7a Andrew Ng v\u00e0 c\u00e1c kho\u00e1 Machine Learning v\u00e0 Deep Learning c\u1ee7a Simplilearn. C\u00e1c b\u00e0i t\u1eadp \u1ee9ng d\u1ee5ng v\u00e0 d\u1eef li\u1ec7u li\u00ean quan \u0111\u01b0\u1ee3c x\u00e2y d\u1ef1ng d\u1ef1a tr\u00ean ngu\u1ed3n h\u1ecdc li\u1ec7u chung c\u1ee7a c\u1ed9ng \u0111\u1ed3ng AI trong v\u00e0 ngo\u00e0i n\u01b0\u1edbc.<\/p>\n\n\n\n

      N\u1ed9i dung kho\u00e1 h\u1ecdc \u0111\u01b0\u1ee3c ph\u00e2n r\u00f5 theo t\u1eebng tu\u1ea7n. M\u1ed7i bu\u1ed5i h\u1ecdc, h\u1ecdc vi\u00ean nh\u1eadn \u0111\u01b0\u1ee3c: (i) N\u1ec1n t\u1ea3ng l\u00fd thuy\u1ebft theo t\u1eebng ch\u1ee7 \u0111\u1ec1; (ii) \u0110\u01b0\u1ee3c h\u01b0\u1edbng d\u1eabn v\u00e0 trao \u0111\u1ed5i l\u00e0m b\u00e0i t\u1eadp th\u1ef1c t\u1ebf li\u00ean quan \u0111\u1ebfn n\u1ed9i dung b\u00e0i h\u1ecdc; (iii) L\u00e0m b\u00e0i t\u1eadp v\u1ec1 nh\u00e0 theo y\u00eau c\u1ea7u.<\/p>\n\n\n\n

      K\u1ebft qu\u1ea3 h\u1ecdc t\u1eadp c\u1ee7a h\u1ecdc vi\u00ean s\u1ebd \u0111\u01b0\u1ee3c \u0111\u00e1nh gi\u00e1 qua 01 b\u00e0i t\u1eadp l\u1edbn gi\u1eefa kho\u00e1 v\u00e0 01 b\u00e0i Test tr\u1eafc nghi\u1ec7m ki\u1ec3m tra ki\u1ebfn th\u1ee9c t\u1ed5ng h\u1ee3p v\u00e0 01 D\u1ef1 \u00e1n cu\u1ed1i kho\u00e1.<\/p>\n\n\n\n

      H\u1ecdc vi\u00ean ho\u00e0n th\u00e0nh t\u1ed1t kho\u00e1 h\u1ecdc s\u1ebd \u0111\u01b0\u1ee3c c\u1ea5p Ch\u1ee9ng ch\u1ec9 c\u1ee7a \u0110\u1ea1i h\u1ecdc Hu\u1ebf v\u00e0 c\u00e1c \u0111\u01a1n v\u1ecb c\u00f9ng ph\u1ed1i h\u1ee3p \u0111\u00e0o t\u1ea1o.<\/p>\n\n\n\n

      2. N\u1ed9i dung chi ti\u1ebft <\/strong><\/h4>\n\n\n\n

      N\u1ed9i d\u1ee5ng kho\u00e1 h\u1ecdc \u0111\u01b0\u1ee3c ph\u00e2n r\u00f5 theo t\u1eebng tu\u1ea7n, c\u1ee5 th\u1ec3:<\/p>\n\n\n\n

      Tu\u1ea7n 1<\/em><\/strong>. Gi\u1edbi thi\u1ec7u AI v\u00e0 Python<\/strong><\/p>\n\n\n\n

      • Gi\u1edbi thi\u1ec7u v\u1ec1 AI, Neural Network, Natural Language Processing<\/li>
      • Ng\u00f4n ng\u1eef l\u1eadp tr\u00ecnh Python v\u00e0 Th\u01b0 vi\u1ec7n NumPy<\/li>
      • Gi\u1edbi thi\u1ec7u Google Colab<\/li><\/ul>\n\n\n\n

        Ph\u1ea7n th\u1ef1c h\u00e0nh<\/em>:<\/p>\n\n\n\n

        • Gi\u1edbi thi\u1ec7u c\u00e1c v\u1ea5n \u0111\u1ec1, \u1ee9ng d\u1ee5ng AI<\/li>
        • Th\u1ef1c h\u00e0nh Python tr\u00ean Google Colab<\/li><\/ul>\n\n\n\n

          Tu\u1ea7n 2<\/em><\/strong>. To\u00e1n cho AI<\/strong><\/p>\n\n\n\n

          • Linear Algebra<\/li>
          • Probability<\/li>
          • Calculus<\/li><\/ul>\n\n\n\n

            Ph\u1ea7n th\u1ef1c h\u00e0nh<\/em>:<\/p>\n\n\n\n

            • L\u00e0m quen c\u00e1c kh\u00e1i ni\u1ec7m, c\u00f4ng th\u1ee9c To\u00e1n v\u1edbi Python.<\/li>
            • X\u00e2y d\u1ee5ng m\u00f4 h\u00ecnh To\u00e1n h\u1ecdc v\u1edbi Python<\/li><\/ul>\n\n\n\n

              Tu\u1ea7n 3<\/em><\/strong>. Machine Learning<\/strong><\/p>\n\n\n\n

              • Introducing Machine Learning<\/li>
              • Supervised Learning<\/li>
              • Unsupervised Learning<\/li>
              • Model Representation<\/li>
              • Cost Function<\/li>
              • Gradient Descent<\/li>
              • Applications of Machine Learning<\/li><\/ul>\n\n\n\n

                Ph\u1ea7n th\u1ef1c h\u00e0nh<\/em>:<\/p>\n\n\n\n

                • Kh\u1ea3o s\u00e1t thu\u1eadt to\u00e1n Gradient Descent<\/li>
                • Ch\u1ea1y th\u1eed m\u1ed9t s\u1ed1 M\u00f4 h\u00ecnh tr\u00ean Google Colab<\/li><\/ul>\n\n\n\n

                  Tu\u1ea7n 4<\/em><\/strong>. Linear Regression<\/strong><\/p>\n\n\n\n

                  • M\u00f4 h\u00ecnh Linear Regression<\/li>
                  • MultiLinear Regression<\/li>
                  • Gradient Descent for Multiple variables<\/li>
                  • Feature Scaling<\/li>
                  • Learning rate<\/li>
                  • Polynomial Regression<\/li>
                  • Normal Equation<\/li><\/ul>\n\n\n\n

                    Ph\u1ea7n th\u1ef1c h\u00e0nh<\/em>:<\/p>\n\n\n\n

                    • X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh Linear Regression<\/li>
                    • Kh\u1ea3o s\u00e1t s\u1ef1 \u1ea3nh h\u01b0\u1edfng c\u1ee7a Learning Rate<\/li>
                    • X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh d\u1ef1 \u0111o\u00e1n gi\u00e1 b\u1ea5t \u0111\u1ed9ng s\u1ea3n<\/li><\/ul>\n\n\n\n

                      Tu\u1ea7n 5<\/em><\/strong>. Logistic Regression<\/strong><\/p>\n\n\n\n

                      • Classification and Representation<\/li>
                      • Logistic Regression Model<\/li>
                      • Multiclass Classification<\/li>
                      • The problem of Overfitting<\/li>
                      • Regularization<\/li><\/ul>\n\n\n\n

                        Ph\u1ea7n th\u1ef1c h\u00e0nh<\/em>:<\/p>\n\n\n\n

                        • Kh\u1ea3o s\u00e1t v\u1ea5n \u0111\u1ec1 Overfitting<\/li>
                        • X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh Logistic Regression v\u00e0 Regularized Logistic Regression cho b\u00e0i to\u00e1n Classification<\/li>
                        • X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh MNIST Logistic Regression nh\u1eadn d\u1ea1ng ch\u1eef s\u1ed1 vi\u1ebft tay<\/li><\/ul>\n\n\n\n

                          Tu\u1ea7n 6. <\/em><\/strong>K-means Clustering v\u00e0 Support Vector Machine<\/strong><\/p>\n\n\n\n

                          K-<\/em><\/strong>means Clustering<\/strong><\/p>\n\n\n\n

                          • Clustering<\/li>
                          • Types of Clustering<\/li>
                          • K-means Clustering<\/li>
                          • Applications of K-means Clustering<\/li>
                          • Distance measure<\/li>
                          • How does K-means Cluster work?<\/li>
                          • K-means Clustering Algorithm<\/li><\/ul>\n\n\n\n

                            Ph\u1ea7n th\u1ef1c h\u00e0nh<\/em>:<\/p>\n\n\n\n

                            • Demo K-means Clustering<\/li>
                            • X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh K-means cho b\u00e0i to\u00e1n Color compression<\/li><\/ul>\n\n\n\n

                              Support Vector Machine<\/em><\/strong><\/p>\n\n\n\n

                              • Applications of Support Vector Machine<\/li>
                              • Why Support Vector Machine?<\/li>
                              • What is Support Vector Machine?<\/li>
                              • Advantages of Support Vector Machine<\/li><\/ul>\n\n\n\n

                                Ph\u1ea7n th\u1ef1c h\u00e0nh:<\/em><\/p>\n\n\n\n

                                • Demo Support Vector Machine<\/li><\/ul>\n\n\n\n

                                  Tu\u1ea7n 7. <\/em><\/strong>Deep Learning v\u00e0 TensorFlow<\/strong><\/p>\n\n\n\n

                                  Deep Learning<\/em><\/strong><\/p>\n\n\n\n

                                  • Deep Learning<\/strong><\/li>
                                  • Applications of Deep Learning<\/strong><\/li>
                                  • What is a Neural Network?<\/strong><\/li>
                                  • Activation Functions<\/strong><\/li>
                                  • Working of a Neural Network<\/strong><\/li>
                                  • Deep Learning frameworks<\/strong><\/li>
                                  • Computer Vision<\/strong><\/li>
                                  • Natural Language Proceesing<\/strong><\/li><\/ul>\n\n\n\n

                                    TensorFlow<\/em><\/strong><\/p>\n\n\n\n

                                    • TensorFlow<\/li>
                                    • Tensors, Tensors Rank<\/li>
                                    • Data Flow graph<\/li>
                                    • Program Elements in TensorFlow: Constant, Variable, Placeholder, Session.<\/li><\/ul>\n\n\n\n

                                      Ph\u1ea7n th\u1ef1c h\u00e0nh<\/em>:<\/p>\n\n\n\n

                                      • Th\u1ef1c h\u00e0nh v\u1edbi TensorFlow: Constant, Variable, Placeholder, Session, Operations.<\/li>
                                      • X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh MNIST Multi-Layer Perception nh\u1eadn d\u1ea1ng ch\u1eef s\u1ed1 vi\u1ebft tay<\/li>
                                      • Kh\u1ea3o s\u00e1t TensorFlow API cho b\u00e0i to\u00e1n Object Detection<\/li><\/ul>\n\n\n\n

                                        Tu\u1ea7n 8<\/em><\/strong>. Neural Network<\/strong><\/p>\n\n\n\n

                                        • Neuron Model and Neural Network<\/li>
                                        • Forward Propagation<\/li>
                                        • Non-linear Classification<\/li>
                                        • Backpropagation algorithm<\/li>
                                        • Training a neural network<\/li><\/ul>\n\n\n\n

                                          Ph\u1ea7n th\u1ef1c h\u00e0nh<\/em>:<\/p>\n\n\n\n

                                          • X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh Logic Gate v\u1edbi Neural Network<\/li>
                                          • Kh\u1ea3o s\u00e1t thu\u1eadt to\u00e1n Backpropagation Algorithm<\/li><\/ul>\n\n\n\n

                                            Tu\u1ea7n 9<\/em><\/strong>. Convolution Neural Networks v\u00e0 Recurrent Neural Network<\/strong><\/p>\n\n\n\n

                                            Convolutional Neural Networks (CNNs)<\/em><\/strong><\/strong><\/p>\n\n\n\n

                                            • Introduction Image Recognition<\/li>
                                            • Convolution Neural Network<\/li>
                                            • Convolution Layer<\/li>
                                            • Relu Layer<\/li>
                                            • Pooling Layer<\/li>
                                            • Flattening<\/li>
                                            • Fully Connected Layer<\/li><\/ul>\n\n\n\n

                                              Ph\u1ea7n th\u1ef1c h\u00e0nh<\/em>:<\/p>\n\n\n\n

                                              • X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh CNN v\u1edbi TensorFlow<\/li>
                                              • X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh nh\u00e2n d\u1ea1ng ch\u1eef s\u1ed1 vi\u1ebft tay v\u1edbi CNN<\/li><\/ul>\n\n\n\n

                                                Recurrent Neural Networks (RNNs)<\/em><\/strong><\/p>\n\n\n\n

                                                • Recurrent Neural Network<\/li>
                                                • Applications of RNN<\/li>
                                                • Representation RNN<\/li>
                                                • Type of RNN <\/li>
                                                • The Vanishing Gradient Problem<\/li>
                                                • The Exploding Gradient Problem<\/li>
                                                • Long Short-Term Memory (LSTM)<\/li>
                                                • Working of LSTM<\/li><\/ul>\n\n\n\n

                                                  Ph\u1ea7n th\u1ef1c h\u00e0nh<\/em>:<\/p>\n\n\n\n

                                                  • X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh Stock Price Prediction v\u1edbi LSTM<\/li>
                                                  • X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh Text Classification v\u1edbi RNN<\/li><\/ul>\n\n\n\n

                                                    Tu\u1ea7n 10<\/em><\/strong>. T\u1ed5ng k\u1ebft v\u00e0 b\u00e0i Test cu\u1ed1i kho\u00e1<\/strong><\/p>\n\n\n\n

                                                    • Mathematica Interview Questions<\/li>
                                                    • Machine Learning Interview Questions<\/li>
                                                    • Deep Learning Interview Questions<\/li>
                                                    • Projects:
                                                      • Face Recognition<\/li><\/ul>
                                                        • Objects Detection<\/li><\/ul>
                                                          • Sentiment Analysis<\/li><\/ul>
                                                            • Text Classification<\/li><\/ul>
                                                              • Word2Vec<\/li><\/ul>
                                                                • Machine Translation<\/li><\/ul><\/li><\/ul>\n","protected":false},"excerpt":{"rendered":"

                                                                  1. Gi\u1edbi thi\u1ec7u Kho\u00e1 \u0111\u00e0o t\u1ea1o mong mu\u1ed1n mang b\u1ea1n \u0111\u1ebfn g\u1ea7n h\u01a1n v\u1edbi cu\u1ed9c c\u00e1ch m\u1ea1ng c\u00f4ng nghi\u1ec7p 4.0, t\u1ea1o c\u01a1 h\u1ed9i \u0111\u1ec3 m\u1ecdi…<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"_links":{"self":[{"href":"https:\/\/jmcspace.com\/index.php\/wp-json\/wp\/v2\/pages\/2074"}],"collection":[{"href":"https:\/\/jmcspace.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/jmcspace.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/jmcspace.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/jmcspace.com\/index.php\/wp-json\/wp\/v2\/comments?post=2074"}],"version-history":[{"count":0,"href":"https:\/\/jmcspace.com\/index.php\/wp-json\/wp\/v2\/pages\/2074\/revisions"}],"wp:attachment":[{"href":"https:\/\/jmcspace.com\/index.php\/wp-json\/wp\/v2\/media?parent=2074"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}