Automated tutorials push

Author: pytorchbot

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1da94067",
+   "id": "b141f85a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "baabd5a8",
+   "id": "3902febc",
    "metadata": {},
    "source": [
     "\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7d34295f",
+   "id": "dcb8e3b4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb3e4dd7",
+   "id": "4216757e",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eecbcc4d",
+   "id": "c26a36a4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf52623e",
+   "id": "efd80a7e",
    "metadata": {},
    "source": [
     "\n",
@@ -605,11 +605,11 @@
     "m2 = torch.tensor([[3., 0.], [0., 3.]]) # three times identity matrix\n",
     "\n",
     "print('\\nVectors & Matrices:')\n",
-    "print(torch.cross(v2, v1)) # negative of z unit vector (v1 x v2 == -v2 x v1)\n",
+    "print(torch.linalg.cross(v2, v1)) # negative of z unit vector (v1 x v2 == -v2 x v1)\n",
     "print(m1)\n",
-    "m3 = torch.matmul(m1, m2)\n",
+    "m3 = torch.linalg.matmul(m1, m2)\n",
     "print(m3)                  # 3 times m1\n",
-    "print(torch.svd(m3))       # singular value decomposition"
+    "print(torch.linalg.svd(m3))       # singular value decomposition"
    ]
   },
   {
@@ -619,6 +619,9 @@
     "This is a small sample of operations. For more details and the full\n",
     "inventory of math functions, have a look at the\n",
     "[documentation](https://pytorch.org/docs/stable/torch.html#math-operations).\n",
+    "For more details and the full inventory of linear algebra operations,\n",
+    "have a look at this\n",
+    "[documentation](https://pytorch.org/docs/stable/linalg.html).\n",
     "\n",
     "Altering Tensors in Place\n",
     "=========================\n",

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5787a3f8",
+   "id": "6ba7b304",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f19dfcf8",
+   "id": "8208d621",
    "metadata": {},
    "source": [
     "\n",

_downloads/be017e7b39198fdf668c138fd8d57abe/tensors_deeper_tutorial.py

@@ -448,17 +448,19 @@
 m2 = torch.tensor([[3., 0.], [0., 3.]]) # three times identity matrix
 
 print('\nVectors & Matrices:')
-print(torch.cross(v2, v1)) # negative of z unit vector (v1 x v2 == -v2 x v1)
+print(torch.linalg.cross(v2, v1)) # negative of z unit vector (v1 x v2 == -v2 x v1)
 print(m1)
-m3 = torch.matmul(m1, m2)
+m3 = torch.linalg.matmul(m1, m2)
 print(m3)                  # 3 times m1
-print(torch.svd(m3))       # singular value decomposition
+print(torch.linalg.svd(m3))       # singular value decomposition
 
 
 ##################################################################################
 # This is a small sample of operations. For more details and the full inventory of
 # math functions, have a look at the
 # `documentation <https://pytorch.org/docs/stable/torch.html#math-operations>`__.
+# For more details and the full inventory of linear algebra operations, have a
+# look at this `documentation <https://pytorch.org/docs/stable/linalg.html>`__.
 # 
 # Altering Tensors in Place
 # ~~~~~~~~~~~~~~~~~~~~~~~~~

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "075de5f3",
+   "id": "0cdf7fe2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1bf8a827",
+   "id": "4631a30d",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "86e2a023",
+   "id": "c1822070",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1aafb454",
+   "id": "6aec81ec",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "119a459a",
+   "id": "21b77c1d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f842017",
+   "id": "8f29e63c",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dba7e04e",
+   "id": "676cfb21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01f1d01b",
+   "id": "45df25e3",
    "metadata": {},
    "source": [
     "\n",